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Girl in tight pajams. Jadakiss the last kiss bootleg download. Man fuck woman on bed. Debra lafave nude pictures. Daring Duo sensual lesbian scene by SapphiX. Lori ramsperger is an asshole. Lesbian pussy to pussy fuck movies. Download free sex and fucking videos for real player. Johnny Sin Fuck Mia Khalifa. Watch Free Category emotion expression face facial SEX Movies In cognitive science and neuroscience, there have been two leading models describing how humans perceive and classify facial expressions of emotion—the continuous and the categorical model. The continuous model defines each facial expression of emotion as a feature vector in a face space. This model explains, for example, how https://hotpants.printablehd.host/page2911-dodefa.php of emotion can be seen at different intensities. In contrast, the Category emotion expression face facial model Category emotion expression face facial of C classifiers, each tuned to a specific emotion category. This model explains, among other findings, why the images in a morphing sequence between a happy and a surprise face are perceived as either happy or surprise but not something in between. While the continuous model has a more difficult time justifying this latter finding, the categorical model is not as good when it comes to explaining how expressions are recognized at different intensities or modes. Most importantly, Category emotion expression face facial models have problems explaining how one can recognize combinations of emotion categories such as happily surprised versus angrily surprised versus surprise. To resolve these issues, in the past several years, we have worked on a revised model that justifies the results reported in the cognitive science and neuroscience literature. This model consists of C distinct continuous spaces. Multiple compound emotion categories can be recognized by linearly combining these C face spaces. The dimensions of these spaces are shown to be mostly configural. According to this model, the major task for the classification of facial expressions of emotion is precise, detailed detection of facial landmarks rather than recognition. We provide an overview of the literature justifying the model, show how the resulting model can be employed to build algorithms for the recognition of facial expression of emotion, and propose research directions in machine learning and computer vision researchers to keep pushing the Category emotion expression face facial of the art in these areas. We also discuss how the model can aid in studies of human perception, social interactions and disorders. The face is an object of major importance in our daily lives. Of primary interest is the production and recognition of facial expressions of emotion. Watch SEX Movies Girl showing asshole.

Slut Sex in Chandrapur. Yet, as much as we understand how facial expressions of emotion are produced, very little is known on how they are interpreted by the human visual system. Without proper models, the scientific studies summarized above as well as the design of intelligent agents and efficient HCI platforms will continue to elude us. A HCI system that can easily recognize expressions of no interest to the human user is of limited interest.

A system that fails to recognize emotions readily identified by us is worse. In the last several years, we have defined a computational model consistent with the cognitive science and neuroscience literature. The present paper presents an overview of this research and a perspective of future areas of interest. We Category emotion expression face facial discuss Category emotion expression face facial machine learning and computer vision should proceed to successfully emulate this capacity in computers and how these models can aid in studies of visual perception, social interactions and disorders such as schizophrenia and autism.

In particular, we provide the following discussion. Category emotion expression face facial model of human perception of facial expressions of emotion: We provide an overview of the cognitive science literature and define a computational model consistent with it. Dimensions of the computational space: Category emotion expression face facial research has shown that human used mostly shape Category emotion expression face facial the perception and recognition of facial check this out of emotion.

In particular, we show that configural features are of much use in this process. A configural feature is defined as a non-rotation invariant modeling of the distance between facial components; for example, the vertical distance between eyebrows and mouth.

We argue that to overcome the current problems of face recognition algorithms including identity and expressionsthe area should make a shift toward a more shape-based modeling. Under this Category emotion expression face facial, the Category emotion expression face facial difficulty for the design of computer vision and machine learning systems is that of precise detection of the features, rather than classification.

We provide a perspective on how to address these problems. The rest of the paper is organized as follows. Section 2 reviews relevant research on the perception of facial expressions of emotion by humans. Section 3 defines a computational model consistent Category emotion expression face facial the results reported in the previous section. Section 4 illustrates the importance of configural read article shape features for the recognition of emotions in face images.

Section 5 argues that the real problem in machine learning and computer vision is a detection one and emphasizes the importance of research in this domain before we can move forward with improved algorithms of face recognition.

In Section 6, we summarize some of the implications of the proposed model. We conclude in Section 7. The human face is an engineering marvel.

Underneath our skin, Category emotion expression face facial large number of muscles allow us to produce many configurations. The face muscles can be summarized as Action Unit AU Ekman and Friesen, https://liveshows.printablehd.host/page4265-vicanu.php positions characteristic of facial expressions of emotion.

These face muscles are more info to the motor neurons in the cerebral cortex through the corticobulbar track. The top muscles are connected bilaterally, while the bottom ones are connected unilaterally to the opposite hemisphere. With proper training, one can learn to move most of the face muscles independently.

Otherwise, facial expressions take on predetermined configurations. There is debate on whether these predetermined configurations are innate or learned nature vs. By universal, we mean that people from different cultures "Category emotion expression face facial" similar muscle movements when expressing some emotions. Facial expressions typically classified as universal are joy, surprise, anger, sadness, disgust and fear Darwin, ; Ekman and Friesen, Universality of emotions is controversial, since it assumes facial expressions of emotion are innate rather than culturally bound.

It also favors a categorical perception of facial expressions of emotion. That is, there is a finite set of predefined classes such as the six listed above. This is known as the categorical model. In the categorical model, we Category emotion expression face facial a set of C classifiers. Each classifier is specifically designed to recognize a single emotion label, such as surprise. Several psychophysical experiments suggest the perception of emotions by humans is categorical Ekman and Rosenberg, Studies in neuroscience further suggest that distinct regions or pathways in the brain are used to recognize different expressions of emotion Calder et al.

An alternative to the categorical model is the continuous model Russell, ; Rolls, Here, each emotion is represented as a feature vector in a multidimensional space given by some characteristics common to all emotions.

This model can justify the perception of many expressions, whereas the categorical model needs to define a class i. It also allows for intensity in the perception of the emotion label. Whereas the categorical model would need to add an additional computation to achieve this goal Martinez,in the continuous model the intensity is intrinsically defined in its representation.

Yet, morphs between expressions of emotions are generally classified to the closest class rather than to an intermediate category Beale and Keil, Perhaps more interestingly, the continuous model better explains the caricature effect Rhodes et al.

Gonzo sexy Watch Sex Videos Masturebating porn. Kim D. Multi-objective based Spatio-temporal feature representation learning robust to expression intensity variations for facial expression recognition. IEEE Trans. Ekman P. Facial Action Coding System: Hamm J. Automated facial action coding system for dynamic analysis of facial expressions in neuropsychiatric disorders. Jeong M. Driver facial landmark detection in real driving situation. Circuits Syst. Video Technol. Tao S. Compound facial expressions of emotion. Benitez-Quiroz C. Kolakowaska A. A review of emotion recognition methods based on keystroke dynamics and mouse movements; Proceedings of the 6th International Conference on Human System Interaction; Gdansk, Poland. Kumar S. Facial expression recognition: Ghayoumi M. A quick review of deep learning in facial expression. Suk M. Ghimire D. Geometric feature-based facial expression recognition in image sequences using multi-class AdaBoost and support vector machines. Happy S. A real time facial expression classification system using local binary patterns; Proceedings of the 4th International Conference on Intelligent Human Computer Interaction; Kharagpur, India. Siddiqi M. Human facial expression recognition using stepwise linear discriminant analysis and hidden conditional random fields. Image Proc. Khan R. Framework for reliable, real-time facial expression recognition for low resolution images. Pattern Recognit. Facial expression recognition based on local region specific features and support vector machines. Tools Appl. Torre F. Polikovsky S. Sandbach G. Static and dynamic 3D facial expression recognition: A comprehensive survey. Zhao G. Facial expression recognition from near-infrared videos. Shen P. Facial expression recognition from infrared thermal videos. Szwoch M. Gunawan A. Face expression detection on Kinect using active appearance model and fuzzy logic. Procedia Comput. Wei W. Tian Y. Deshmukh S. Survey on real-time facial expression recognition techniques. IET Biom. Mavadati S. A spontaneous facial action intensity database. Maalej A. Shape analysis of local facial patches for 3D facial expression recognition. Yin L. Lyons M. LeCun Y. Backpropagation applied to handwritten zip code recognition. Neural Comput. Genetic algorithm based filter bank design for light convolutional neural network. Breuer R. A deep learning perspective on the origin of facial expressions. Jung H. Zhao K. Olah C. Donahue J. Long-term recurrent convolutional networks for visual recognition and description. Pattern Anal. Chu W. Hasani B. Graves A. Facial expression recognition with recurrent neural networks; Proceedings of the International Workshop on Cognition for Technical Systems; Santorini, Greece. Jain D. Multi angle optimal pattern-based deep learning for automatic facial expression recognition. Yan W. An improved spontaneous micro-expression database and the baseline evaluation. Zhang X. A high resolution spontaneous 3D dynamic facial expression database. Kohavi R. Ding X. Huang M. Zhen W. Zhang S. Robust facial expression recognition via compressive sensing. Dynamic texture recognition using local binary patterns with an application to facial expressions. Jiang B. Lee S. Collaborative expression representation using peak expression and intra class variation face images for practical subject-independent emotion recognition in videos. Liu M. Deeply learning deformable facial action parts model for dynamic expression analysis; Proceedings of the Asian Conference on Computer Vision; Singapore. AU-inspired deep networks for facial expression feature learning. Mollahosseini A. Recent advances in convolutional neural networks. Support Center Support Center. External link. Please review our privacy policy. Sadly surprised. Compound emotion [ 17 ]. Seven emotions and 22 compound emotions. Distribution between each pair of fiducials Appearance defined by Gabor filters. Nearest-mean classifier, Kernel subclass discriminant analysis. EmotioNet [ 18 ]. Euclidean distances between normalized landmarks Angles between landmarks Gabor filters centered at of the landmark points. Kernel subclass discriminant analysis. Real-time mobile [ 22 ]. Seven emotions. Active shape model fitting landmarks Displacement between landmarks. Ghimire and Lee [ 23 ]. Displacement between landmarks in continuous frames. Global Feature [ 24 ]. Six emotions. Local binary pattern LBP histogram of a face image. Principal component analysis PCA. Local region specific feature [ 33 ]. United States: McGraw-Hill, A Contribution to the Ontogenesis of Social Relations. American Sign Language: A teacher's resource text on grammar and culture. Silver Spring, MD: The linguistics of British Sign Language. Friesen; P. Ellsworth Guidelines for research and a review of findings". New York: Experimental Psychology. Henry Holt. A review of the cross-cultural studies". Emotion Review. Proceedings of the National Academy of Sciences. Friesen Journal of Personality and Social Psychology. Facial Action Coding System 3. Manual of Scientific Codification of the Human Face. Russell Judging emotion from the face in context". September Journal of Nonverbal Behavior. Darwin and facial expression: Cambridge, MA: Malor Books. CS1 maint: Extra text: Physiology of behavior 11th ed. New Jersey: Pearson Education Inc. Multiple names: A Review of the Cross-Cultural Studies". Authority control GND: Retrieved from " https: Facial expressions Anatomical simulation Emotion. Hidden categories: Namespaces Article Talk. Views Read Edit View history. In other projects Wikimedia Commons Wikiquote. The pictures were selected according to their photographic quality and expression quality evaluated by a trained researcher and FaceReader software codes see validation below. Expression elicitation was structured into three phases; the three phases were completed for one emotion before starting with the production of the next emotional expression. The sequence was: The first expression elicitation phase was the Emotion Induction Phase. Models were asked to carefully look at the pictures, identify which emotion the picture elicited in them, and display that emotion in their face with the intention to communicate it spontaneously. Models were also instructed to communicate the emotion with their face at a level of intensity that would make a person not seeing the stimulus picture understand what emotion the picture elicited in them. We used four neutral, five sadness, six disgust, seven fear, and four happiness inducing IAPS pictures. Since there are no IAPS pictures for anger, the induction phase for anger started with the second phase. After all pictures within one emotion were presented continuously, models were asked to choose one of the pictures for closer inspection. During the inspection of the selected picture further photographs were taken. The second expression elicitation phase was the Personal Experience Phase. Models were asked to imagine a personally relevant episode of their lives in which they strongly experienced a certain emotional state corresponding to one of the six emotions happiness, surprise, fear, sadness, disgust, and anger. The instructions were the same as in the first phase: The third expression elicitation phase was the Imitation Phase. Models were instructed by written and spoken instructions based on emotion descriptions according to Ekman and Friesen regarding how to exert the specific muscular activities required for expressing the six emotions in the face. In contrast to the previous phases, no emotional involvement was necessary in the imitation part. Models were guided to focus on the relevant areas around the eyes, the nose, and the mouth and instructed on how to activate these regions in order to specifically express one of the six basic emotions. During this phase photographers continuously supported the models by providing them with feedback. Models were also requested to inspect their facial expression in a mirror. They had the opportunity to compare their own expression with the presented expression model from the database by Ekman and Friesen and to synchronize their expression with a projected prototypical emotion portrait. First, trained researchers and student assistants selected those pictures that had an acceptable photographic quality. From all selected pictures those that clearly expressed the intended emotion, including low intensity pictures, were selected for all models. Facial expressions were coded regarding the target emotional expression along with the other five basic emotion expressions and neutral expressions using FaceReader 3 software http: Based on FaceReader 3 emotion ratings, the stimuli were assigned to the tasks described below. Overall accuracy rate of FaceReader 3 at classifying expressions of younger adults is estimated 0. Happiness 0. During post-processing of the images, differences in skin texture were adjusted and non-facial cues, like ears, hair and clothing, were eliminated. Physical attributes like luminance and contrast were held constant across images. Each task was balanced with an equal number of female and male stimuli. Whenever two different identities were simultaneously presented in a given trial, portraits of same sex models were used. All tasks were administered by trained proctors in group-sessions with up to 10 participants. Sessions were completed in approximately weekly intervals. Both task and trial sequences were kept constant across all participants. Computers with inch monitors screen definition: The tasks were programmed in Inquisit 3. Each task started at the same time for all participants in a given group. In general, participants were asked to work to the best of their ability as quickly as possible. They were instructed to use the left and right index fingers during tasks that used two response options and to keep the fingers positioned directly above the relevant keys throughout the whole task. Tasks with four response options were organized such that the participant only used the index finger of a preferred hand. Every single task was introduced by proctors and additional instructions were provide on screen. There were short practice blocks in each task consisting of at least 5 and at most 10 trials depending on task difficulty with trial-by-trial feedback about accuracy. There was no feedback for any of the test trials. Table 1 gives an overview of the tasks included in the task battery. Outliers in univariate distributions were set to missing. For the approximately 0. With this procedure, plausible values were computed as predicted values for missing observations plus a random draw from the residual normal distribution of the respective variable. One of the multiple datasets was used for the analyses reported here. Results were verified and do not differ from datasets obtained through multiple imputation with the R package mice, by van Buuren and Groothuis-Oudshoorn Reaction time RT scores were only computed from correct responses. RTs smaller than ms were set to missing, because they were considered too short to represent proper processing. The remaining RTs were winsorized e. This procedure was repeated iteratively beginning with the slowest response until there were no more RTs above the criterion of 3 SD. All analyses were conducted with the statistical software environment R. For accuracy tasks, we defined the proportion of correctly solved trials of an experimental condition of interest e. For some of these tasks we applied additional scoring procedures as indicated in the corresponding task description. Speed indicators were average inverted RTs measures in seconds obtained across all correct responses associated with the trials from the experimental conditions of interest. Note that accuracy was expected to be at ceiling in measures of speed. Inverted latency was calculated as divided by the RT in milliseconds. Calder et al. Composite facial expressions were created by aligning the upper and the lower face half of the same person, but from photos with different emotional expressions, so that in the final photo each face was expressing an emotion in the upper half of the face that differed from the emotion expressed in the lower half of the face. Aligned face halves of incongruent expressions lead to holistic interference. It has been shown that an emotion expressed in only one face half is less accurately recognized compared to congruent emotional expressions in face composites e. In order to avoid ceiling effects, as is common for the perception of emotions from prototypical expressions, we took advantage of the higher task difficulty imposed by combining different facial expressions in the top and bottom halves of faces, and exploited the differential importance of the top and bottom face for the recognition of specific emotions Ekman et al. Specifically, fear, sadness, and anger are more readily recognized in the top half of the face and happiness, surprise, and disgust in the bottom half of the face Calder et al. Here, we used the more readily recognizable halves for the target halves in order to ensure acceptable performance. Top halves expressing fear, sadness, or anger were only combined with bottom halves expressing disgust, happiness, or surprise—yielding nine different composites see Figure 1 for examples of all possible composite expression stimuli of a female model. Figure 1. Stimuli examples used in Task 1 Identification of emotion expression from composite faces. After the instruction and nine practice trials, 72 experimental trials were administered. The trial sequence was random across the nine different emotion composites. Pictures with emotional expressions of four female and four male models were used to create the 72 emotion composites. For each model, nine aligned composite faces were created. In each trial, following a fixation cross, a composite face was presented in the center of the screen. Participants were asked to click with a computer mouse the button corresponding to the emotion in the prompted face half. After the button was clicked the face disappeared and the screen remained blank for ms; then the next trial started with the fixation cross. In addition to the proportion of correct responses across a series of 72 trials, we calculated unbiased hit rates H u ; Wagner, Unbiased hit rates account for response biases toward a specific category and correct for systematic confusions between emotion categories. For a specific emotion score H u was calculated as squared frequency of the correct classifications divided by the product of the number of stimuli used for the different emotion categories and the overall frequency of choices for the target emotion category. We report difficulty estimates for both percent correct and H u. We calculated reliabilities on the basis of percent correct scores. Difficulty estimates in Table 2 based on percent correct scores show that performance was not at ceiling. Post-hoc analyses indicate happiness was recognized the best, followed by surprise, anger, disgust, fear, and sadness. This ranking was similar for H u scores see Table 2. However, when response biases were controlled for, anger was recognized better than surprise. Percent correct and H u scores across all trials were correlated 0. Table 2. Descriptive statistics and reliability estimates of performance accuracy for all emotion perception tasks across all trials and for single target emotions. Reliability estimates across all trials were very good and across all trials for a single emotion, considering the low number of trials for single emotions and the unavoidable heterogeneity of facial stimuli, were satisfactory ranging between 0. Difficulty estimates suggest that performance across persons was not at ceiling. The psychometric quality of single emotion expression scores and performance on the overall measure are satisfactory to high. Adding more trials to the task could further increase the reliability of the emotion specific performance indicators. Motion facilitates emotion recognition from faces e. Kamachi et al. Ambadar et al. In Task 2, we used dynamic stimuli in order to extend the measurement of emotion identification to more real life-like situations and to ensure adequate construct representation of the final task battery Embretson, Because previous findings predict higher accuracy rates for emotion identification from dynamic stimuli, we implemented intensity manipulations in order to avoid ceiling effects. Hess et al. We generated expression-end-states by morphing intermediate expressions between a neutral and an emotional face. Mixture ratios for the morphs aimed at three intensity levels by decreasing the proportion of neutral relative to the full emotion expressions from In order to capture the contrast between configural vs. Face inversion strongly impedes holistic processing, allowing mainly feature-based processing Calder et al. McKelvie indicated an increase of errors and RTs of emotion perception from static faces presented upside-down and similar findings were reported for dynamic stimuli as well Ambadar et al. The first frame of the video displayed a neutral facial expression that, across the subsequent frames, changed to an emotional facial expression. The videos ended at ms and the peak expression displayed in the last frame remained on the screen until the categorization was performed. Emotion label buttons were the same as in the previous task. We varied expression intensity across trials, with one third of the trials for each intensity level. The morphing procedure was similar to the procedure used in previous studies e. First, static pictures were generated by morphing a neutral expression image of a face model with the images of the same person showing one of the 6 basic emotions; mixture ratios were 40, 60, or 80 percent of the emotional face. Second, short video sequences were produced on the basis of a morphed sequence of frames starting from a neutral expression and ending with one of emotional faces generated in the first step. Thus, video sequences were created for all three intensities; this was done separately for two female and two male models. Half of the 72 trials were presented upright and the other presented upside down. Following the instructions participants completed four practice trials. The experimental trials with varying conditions upright vs. In addition to results for the percent correct scores, we also report unbiased hit rates see above. Table 2 summarizes the average performance calculated for both, percent correct and unbiased hit rates the scores are correlated 0. It seems that the facial expressions of anger used here were particularly heterogeneous. There were no ceiling effects in any of the indicators. An rmANOVA with factors for emotion expression and expression intensity revealed main effects for both. The rank order of recognizability of different emotional expressions was comparable with Task 1, which used expression composites cf. Figures 2A,B. Happiness and surprise were recognized the best, followed by anger and disgust, and finally sadness and fear were the most difficult. Scores calculated across all trials within single emotions disregarding the intensity manipulation had acceptable or good psychometric quality. Figure 2. Plots of the rank order of recognizability of the different emotion categories esteemed in emotion perception task. A Task 1, Identification of Emotion Expression from composite faces; B Task 2, Identification of Emotion Expression of different intensity from upright and inverted dynamic face stimuli; C Task 3, Visual search for faces with corresponding Emotion Expression of different intensity, error bars represent confidence intervals. Task 3 was inspired by the visual search paradigm often implemented for investigating attention biases to emotional faces e. In general, visual search tasks require the identification of a target object that differs in at least one feature e. In this task, participants had to recognize several target facial expressions that differed from a prevailing emotion expression. Usually, reaction time slopes are inspected as dependent performance variables in visual search tasks. However, we set no limits on response time and encouraged participants to screen and correct their responses before confirming their choice. This way we aimed to minimize the influence of visual saliency of different emotions on the search efficiency due to pre-attentive processes Calvo and Nummenmaa, and capture intentional processing instead. This task assessed the ability to discriminate between different emotional facial expressions. The majority of the images displayed one emotional expression surprise, fear, sadness, disgust, or anger referred to here as the target expression. In each trial participants were asked to identify the neutral and emotional expressions. Experimental manipulations incorporated in each trial were: Happiness expressions were not used in this task because performance for smiling faces was assumed to be at ceiling due to pop out effects. The location of target stimuli within the grid was pseudo-randomized. Participants' task was to identify and indicate all distracter expressions by clicking with their mouse a tick box below each stimulus. The task aimed to implement two levels of difficulty by using target and distracter expressions with low and high intensity. Figure 3. Schematic representation of a trail from Task 3 Visual search for faces with corresponding emotion expression of different intensity. All stimuli were different images originating from four models two females and two males. Intensity level was assessed with the FaceReader software. Based on these intensity levels, trials were composed of either low or high intense emotion stimuli for targets as well as for distracters within the same trial. The number of divergent expressions to be identified was distributed uniformly across conditions. There were 40 experimental trials administered after three practice trials, which followed the instructions. The accuracies of the multiple answers for a trial are dependent variables. We applied three different scoring procedures. The first was based on the proportion of correctly recognized targets. This procedure only accounts for the hit rates, disregards false alarms, and can be used to evaluate the detection rate of target facial expressions. For the second, we computed a difference score between the hit-rate and false-alarm rate for each trial. This score is an indicator of the ability to recognize distracter expressions. Next, we will report proportion correct scores. Table 2 additionally displays average performance based on the d'prime scores. The univariate distributions of emotion-specific performance indicators and the average performance—displayed in Table 2 —suggest substantial individual differences in accuracy measures. The task design was successful at avoiding ceiling effects frequently observed for recognition performance of prototypical expressions. This was presumably achieved by using stimuli of varying expression intensity and by the increasing number of distracters across trials. Considering that only eight trials entered the emotion specific scores and that emotional expressions are rather heterogeneous, reliability estimates ranging from 0. The rank orders of recognizability of the emotion categories were slightly different from those estimated in Task 1 and 2 see Figures 2C,B compared with Figures 2A,B. Surprised faces were recognized the best, as was the case for Task 2. Anger faces were recognized considerably worse than sadness faces. This inconsistency might be due to effects of stimulus sampling. Performance on fear expressions was the poorest. The difficulty manipulation based on high vs. The ratios of the difference between low and high intensity conditions varied across emotions: We conclude that performance indicators derived from this task have acceptable psychometric quality. Empirical difficulty levels differ across the intended manipulations based on expression intensity and the task revealed a rank order of recognizability similar to other tasks used in this study. The scoring procedure hardly affected the rank order of persons, allowing the conclusion that different scores derived from this task express the same emotional expression discrimination ability. It is suggested that the encoding of facial emotion expressions is based on discrete categorical qualitative matching Etcoff and Magee, ; Calder et al. There is evidence that both types of perception are integrated and used complementary Fujimura et al. In this task, we required participants to determine the mixture ratios of two prototypical expressions of emotions. In order to avoid memory-related processes we constructed a simultaneous matching task. We morphed expressions of two emotions along a continuum of 10 mixture ratios. We only morphed continua between adjacent emotions on a so-called emotion hexagon with the sequence happiness-surprise-fear-sadness-disgust-anger , where proximity of emotions represents potentially stronger confusion between expressions e. In terms of categorical perception, there should be an advantage in identifying the correct mixture-ratio at the end of a continuum compared with more balanced stimuli in the middle of the continuum between two expression categories Calder et al. Morphed images were created from two different expressions with theoretically postulated and empirically tested maximal confusion rates Ekman and Friesen, Thus, morphs were created on the following six continua: These morphs were created for each face separately for five female and five male models. In every trial, two images of the same identity were presented on the upper left and on the upper right side of the screen, where each image displayed a different prototypical emotion expression happiness, surprise, fear, sadness, disgust, and anger. Below these faces, centered on the screen, was a single expression morphed from the prototypical faces displayed in the upper part of the screen. All three faces remained on the screen until participants responded. Participants were asked to estimate the ratio of the morphed photo on a continuous visual analog scale. Participants were asked to estimate the mixture-ratio of the morph photo as exactly as possible, using the full range of the scale. There were no time limits. Three practice trials preceded 60 experimental trials. We scored performance accuracy as the average absolute deviation of participants' response from the correct proportion of the mixture between the two parent expressions. Table 2 displays the average overall and emotion specific deviation scores. Further, it was interesting to investigate whether performance was higher toward the ends of the continua as predicted by categorical accounts of emotional expression perception. A series of two-tailed paired t -tests compared differences between the emotion categories of the parent photo. The correct mixture ratio was better identified in the following combinations: Generally, we expected mixtures of more similar expressions to bias the evaluation of the morphs. The results are essentially in line with these expectations based on expression similarities. Taken together, the results suggest the deviation scores meet psychometric standards. Performance improved or worsened as predicted by theories of categorical perception. Future research should examine whether expression assignment in morphed emotions is indicative of the ability to identify prototypical emotion expressions. This task is a forced choice version of the previously described Task 4 and aims to measure categorical perception of emotional expressions using a further assessment method. Participants were asked to decide whether the morphed expression presented in the upper middle of the screen was more similar to the expression prototype displayed on the lower left or lower right side of the screen. Stimuli were identical with those used in Task 4, but the sequence of presentation was different. The task design differed from that of Task 4 only in that participants were forced to decide whether the expression-mix stimulus was composed of more of the left or more of the right prototypical expression. Response keys were the left and right control keys on the regular computer keyboard, which were marked with colored tape. The average percentages of correct decisions are given in Table 2. This task was rather easy compared with Tasks 1—3. The distribution of the scores was, however, not strongly skewed to the right, but rather followed a normal distribution with most of the participants performing within the range of 0. Similarly to Task 4, the rank order of emotion recognizability was not similar to Tasks 1 or 2. Generally, the psychometric properties of this task need improvement and further studies should address the question whether forced-choice expression assignment in emotion-morphs is indicating the same ability factor indicated by the other tasks i. The following five tasks arguably assess individual differences in memory-related abilities in the domain of facial expressions. All tasks consist of a learning phase for facial expressions and a subsequent retrieval phase that requires recognition or recall of previously learned expressions. The first three memory tasks include an intermediate task between learning and recall of at least three minutes, hence challenging long-term retention. In Task 9 and 10, learning is immediately followed by retrieval. With this forced-choice SM task we aimed to assess the ability to learn and recognize facial expressions of different intensity. Emotion category, emotion intensity, and learning-set size varied across trials, but face identity was constant within a block of expressions that the participant was asked to learn together. Manipulations of expression intensity within targets, but also between targets and distracters, were used to increase task difficulty. The recognition of expression intensity is also a challenge in everyday life; hence, the expression intensity manipulation is not restricted to psychometric rationales. We expected hit-rates to decline with increasing ambiguity for less intense targets e. We administered one practice block of trials and four experimental blocks—including four face identities half were females and 18 trials per block. But this can only be achieved with a shape change. Hence, our face spaces should include both, configural and shape features. It is important to note that configural features can be obtained from an appropriate representation of shape. Expressions such as fear and disgust seem to be mostly if not solely based on shape features, making recognition less accurate and more susceptible to image manipulation. We have previously shown Neth and Martinez, that configural cues are amongst the most discriminant features in a classical Procrustes shape representation, which can be made invariant to 3D rotations of the face Hamsici and Martinez, a. Thus, each of the six categories of emotion happy, sad, surprise, angry, fear and disgust is represented in a shape space given by classical statistical shape analysis. First the face and the shape of the major facial components are automatically detected. This includes delineating the brows, eyes, nose, mouth and jaw line. The shape is then sample with d equally spaced landmark points. The mean center of mass of all the points is computed. The 2 d -dimensional shape feature vector is given by the x and y coordinates of the d shape landmarks subtracted by the mean and divided by its norm. This provides invariance to translation and scale. The dimensions of each emotion category can now be obtained with the use of an appropriate discriminant analysis method. We use the algorithm defined by Hamsici and Martinez because it minimizes the Bayes classification error. As an example, the approach detailed in this section identifies the distance between the brows and mouth and the width of the face as the two most important shape features of anger and sadness. It is important to note that, if we reduce the computational spaces of anger and sadness to 2-dimensions, they are almost indistinguishable. Thus, it is possible that these two categories are in fact connected by a more general one. This goes back to our question of the number of basic categories used by the human visual system. The face space of anger and sadness is illustrated in Figure 5 , where we have also plotted the feature vectors of the face set of Ekman and Friesen We also plot the images of anger and sadness of Ekman and Friesen In dashed are simple linear boundaries separating angry and sad faces according to the model. This continuous model is further illustrated in b. Note that, in the proposed computational model, the face space defining sadness corresponds to the right-bottom quadrant, while that of anger is given by the left-top quadrant. As in the above, we can use the shape space defined above to find the two most discriminant dimensions separating each of the six categories listed earlier. The resulting face spaces are shown in Figure 6. In each space, a simple linear classifier in these spaces can successfully classify each emotion very accurately. To test this, we trained a linear support vector machine Vapnik, and use the leave-one-out test on the data set of images of Ekman and Friesen Of course, adding additional dimensions in the feature space and using nonlinear classifiers can readily achieve perfect classification i. The important point from these results is to note that simple configural features can linearly discriminate most of the samples in each emotion. These features are very robust to image degradation and are thus ideal for recognition in challenging environments e. Shown in the above are the six feature spaces defining each of the six basic emotion categories. A simple linear Support Vector Machine SVM can achieve high classifica-tion accuracies; where we have used a one-versus-all strategy to construct each classifier and tested it using the leave-one-out strategy. Here, we only used two features dimensions for clarity of presentation. Higher accuracies are obtained if we include additional dimensions and training samples. As seen thus far, human perception is extremely tuned to small configural and shape changes. If we are to develop computer vision and machine learning systems that can emulate this capacity, the real problem to be addressed by the community is that of precise detection of faces and facial features Ding and Martinez, Classification is less important, since this is embedded in the detection process; that is, we want to precisely detect changes that are important to recognize emotions. Most computer vision algorithms defined to date provide, however, inaccurate detections. One classical approach to detection is template matching. In this approach, we first define a template e. This template is learned from a set of sample images; for example, estimating the distribution or manifold defining the appearance pixel map of the object Yang et al. Detection of the object is based on a window search. That is, the learned template is compared to all possible windows in the image. If the template and the window are similar according to some metric, then the bounding box defining this window marks the location and size scale of the face. The major drawback of this approach is that it yields imprecise detections of the learned object, because a window of an non-centered face is more similar to the learned template than a window with background say, a tree. An example of this result is shown in Figure 7. A solution to the above problem is to learn to discriminate between non-centered windows of the objects and well centered ones Ding and Martinez, In this alternative, a non-linear classifier or some density estimator is employed to discriminate the region of the feature space defining well-centered windows of the objects and non-centered ones. This features versus context idea is illustrated in Figure 8. This approach can be used to precisely detect faces, eyes, mouth, or any other facial feature where there is a textural discrimination between it and its surroundings. Figure 9 shows some sample results of accurate detection of faces and facial features with this approach. The idea behind the features versus context approach is to learn to discriminate between the feature we wish to detect e. This approach eliminates the classical overlapping of multiple detections around the object of interest at multiple scales. At the same time, it increases the accuracy of the detection because we are moving away from poor detections and toward precise ones. Precise detections of faces and facial features using the algorithm of Ding and Martinez, The same features versus context idea can be applied to other detection and modeling algorithms, such as Active Appearance Models AAM Cootes et al. One obvious limitation is that the learned model is linear. A solution to this problem is to employ a kernel map. Kernel PCA is one option. Once we have introduced a kernel we can move one step further and use it to address additional issues of interest. A first capability we may like to add to a AAM is the possibility to work with three-dimensions. The second could be to omit the least-squares iterative nature of the Procrustes alignment required in most statistical shape analysis methods such as AAM. RIK add yet another important advantage to shape analysis: Thus, once the shape is been mapped to the RIK space, objects e. By now we know that humans are very sensitive to small changes. But we do not yet know how sensitive or accurate. Of course, it is impossible to be pixel accurate when marking the boundaries of each facial feature, because edges blur over several pixels. This can be readily observed by zooming in the corner of an eye. To estimate the accuracy of human subjects, we performed the following experiment. First, we designed a system that allows users to zoom in at any specified location to facilitate delineation of each of the facial features manually. Second, we asked three people herein referred to as judges to manually delineate each of the facial components of close to 4, images of faces. Third, we compared the markings of each of the three judges. The within-judge variability was on average 3. This gives us an estimate of the accuracy of the manual detections. The average error of the algorithm of Ding and Martinez is 7. Thus, further research is needed to develop computer vision algorithms that can extract even more accurate detection of faces and its components. Another problem is what happens when the resolution of the image diminishes. Humans are quite robust to these image manipulations Du and Martinez, One solution to this problem is to use manifold learning. In particular, we wish to define a non-linear mapping f. This is illustrated in Figure That is, given enough sample images and their shape feature vectors described in the preceding section, we need to find the function which relates the two. This can be done, for example, using kernel regression methods Rivera and Martinez, One of the advantages of this approach is that this function can be defined to detect shape from very low resolution images or even under occlusions. Example detections using this approach are shown in Figure Manifold learning is ideal for learning mappings between face object images and their shape description vectors. Shape detection examples at different resolutions. Note how the shape estimation is almost as good regardless of the resolution of the image. Recent advances in non-rigid structure from motion allow us to recover very accurate reconstructions of both the shape and the motion even under occlusion. A recent approach resolves the nonlinearity of the problem using kernel mappings Gotardo and Martinez, b. Combining the two approaches to detection defined in this section should yield even more accurate results in low-resolution images and under occlusions or other image manipulations. We hope that more research will be devoted to this important topic in face recognition. The approaches defined in this section are a good start, but much research is needed to make these systems comparable to human accuracies. We argue that research in machine learning should address these problems rather than the typical classification one. A first goal is to define algorithms that can detect face landmarks very accurately even at low resolutions. Kernel methods and regression approaches are surely good solutions as illustrated above. But more targeted approaches are needed to define truly successful computational models of the perception of facial expressions of emotion. In the real world, occlusions and unavoidable imprecise detections of the fiducial points, among others, are known to affect recognition Torre and Cohn, ; Martinez, Additionally, some expressions are, by definition, ambiguous. Most importantly though seems to be the fact that people are not very good at recognizing facial expressions of emotion even under favorable condition Du and Martinez, Humans are very robust at detection joy and surprise from images of faces; regardless of the image conditions or resolution. However, we are not as good at recognizing anger and sadness and are worst at fear and disgust. The above results suggest that there could be three groups of expressions of emotion. The first group is intended for conveying emotions to observers. These expressions have evolved a facial construct i. Example expressions in this group are happiness and surprise. A computer vision system—especially a HCI—should make sure these expressions are accurately and robustly recognized across image degradation. Therefore, we believe that work needs to be dedicated to make systems very robust when recognizing these emotions. The second group of expressions e. A computer vision system should recognize these expressions in good quality images, but can be expected to fail as the image degrades due to resolution or other image manipulations. An interesting open question is to determine why this is the case and what can be learned about human cognition from such a result. The third and final group of emotions constitutes those at which humans are not very good recognizers. This includes expressions such as fear and disgust. Early work especially in evolutionary psychology had assumed that recognition of fear was primal because it served as a necessary survival mechanism LeDoux, Recent studies have demonstrated much the contrary. Fear is generally poorly recognized by healthy human subjects Smith and Schyns, ; Du and Martinez, .

This is because the farther the feature vector representing that Category emotion expression face facial is from the mean or center of the face spacethe easier it is to recognize it Valentine, Category emotion expression face facial neuroscience, the multidimensional or continuous view of emotions was best exploited under the limbic hypothesis Calder et al.

Under this model, there should click here a neural mechanism responsible for the recognition of all facial expressions of emotion, which was assumed to take place in click limbic system.

Recent results have however uncovered dissociated networks for the recognition of most emotions. This is not necessarily proof of a categorical model, but it strongly suggests that there are at least distinct groups of emotions, each following distinct interpretations. Furthermore, humans are only very good at recognizing a number of facial expressions of emotion. The most readily recognized emotions are happiness and surprise.

It has been shown that joy and surprise can be robustly identified extremely accurately at almost any resolution Du and Martinez, Figure 1 shows a happy expression at four different resolutions.

The reader should not have any problem recognizing the emotion in display even at the lowest Category emotion expression face facial resolutions. However, humans are not as good at recognizing anger and sadness and are even worse at fear and disgust. Happy faces at four different resolutions. From left see more right: All images have been resized to a common image size for visualization.

A major question of interest is the following. Why are some facial configurations more easily recognizable than others? One possibility is that expressions such as joy and surprise involve larger face transformations than the others. This has recently proven not to be the case Du and Martinez, While surprise does have the largest Category emotion expression face facial, this is followed by disgust and fear which are poorly recognized.

Gratis sexi Watch Porn Videos Miif videos. Viewpoint effects were as expected and contradict the results from Matsumoto and Hwang Expressions were recognized significantly better if the expression was learned with frontal rather than a three quarter view: We recommend using face orientation as a difficulty manipulation and the overall performance across trials as indicator of expression recognition. With this task, we intended to assess recognition performance of mixed—rather than prototypical—facial expressions. It was not our aim to test theories that postulate combinations of emotions that result in complex affect expressions, such as contempt, which is proposed as a mixture of anger and disgust or disappointment, which is proposed as a combination of surprise and sadness cf. Plutchik, Instead, we aimed to use compound emotion expressions to assess the ability to recognize less prototypical, and to some extent more real-life, expressions. Furthermore, these expressions are not as easy to label as the basic emotion expressions parenting the mixed expressions. Therefore, for the mixed emotions of the present task we expect a smaller contribution of verbal encoding to task performance, as has been reported for face recognition memory for basic emotions Nakabayashi and Burton, We used nine different combinations of six basic emotions Plutchik, Within each block of trials, the images used for morphing mixed expressions were from a single identity. Across blocks, sex of the identities was balanced. There were four experimental blocks preceded by a practice block. The number of stimuli to be learned ranged from two targets in Block 1 to five targets in Block 4. The presentation time of the targets during the learning period changed depending on the number of targets displayed, ranging from 30 to 60 s. Across blocks, 11 targets showed morphed mixture ratios of During the learning phase, stimuli were presented simultaneously on the screen. During a delay period of approximately three minutes, participants answered a subset of questions from the Trait Meta Mood Scale Salovey et al. At retrieval, participants saw a pseudo-randomized sequence of images displaying mixed expressions. Half of the trials were learned images. The other trials differed from the learned targets in the expression mixture, in the mixture ratio, or both. There were 56 recall trials in this task. The same scoring procedures were used as in Task 7. The average performance over all trials see Table 3 was well above chance. Different scoring procedures hardly affected the rank order of individuals within the sample; the proportion correct scores were highly correlated with the d'prime scores 0. Reliability estimates suggest good psychometric quality. Further studies are needed to investigate whether learning and recognizing emotion-morphs are tapping the same ability factor as learning and recognizing prototypical expressions of emotion. Because expectations on mean differences at recognizing expression morphs are difficult to derive from a theoretical point of view, we only consider the psychometric quality of the overall score for this task. Memory span paradigms are frequently used measures of primary memory. The present task was designed as a serial cued memory task for emotion expressions of different intensity. Because recognition was required in the serial order of the stimuli displayed at learning, the sequence of presentation served as a temporal-context for memorizing facial expressions. We used FaceReader see above to score intensity levels of the stimuli chosen for this task. We used three male and four female identities throughout the task, with one identity per block. The task began with a practice block followed by seven experimental blocks of trials. Each block started with a sequence of facial expressions happiness, surprise, fear, sadness, disgust, and anger , presented one at a time, and was followed immediately by the retrieval phase. The sequence of targets at retrieval was the same as the memorized sequence. Participants were suggested to use the serial position as memory cue. Number of trials within a sequence varied between three and six. Most of the targets 25 of 33 images and distracters 37 of 54 images displayed high intensity prototypical expressions. During the learning phase stimulus presentation was fixed to ms, followed by a blank inter-stimulus interval of another ms. The position of the target in this matrix varied across trials. Distracters within a trial differed from the target in its emotional expression, intensity, or both. Participants indicated the learned expression via mouse click on the target image. Table 3 provides performance and reliability estimates. Average performance ranged between 0. Reliability estimates for the entire task are acceptable; reliability estimates for the emotion-specific trials were low; increasing the number of trials could improve reliabilities for the emotion-specific trials. We therefore recommend the overall percentage correct score as a psychometrically suitable measure of individual differences of primary memory for facial expressions. The task was to quickly detect the particular pairs and to memorize them in conjunction with their spatial arrangement on the screen. Successful detection of the pairs requires perceptual abilities. During retrieval, one expression was automatically disclosed and participants had to indicate the location of the corresponding expression. Future work might decompose perceptual and mnestic demands of this task in a regression analysis. At the beginning of a trial block several expressions initially covered with a card deck appeared as a matrix on the screen. During the learning phase, all expressions were automatically disclosed and participants were asked to detect expression pairs and to memorize their location. Then, after several seconds, the learning phase was stopped by the program, and again the cards were displayed on the screen. Next, one image was automatically disclosed and participants indicated the location of the corresponding expression with a mouse click. After the participant's response, the clicked image was revealed and feedback was given by encircling the image in green correct or red incorrect. Two seconds after the participant responded, the two images were again masked with the cards, and the next trial started by program flipping over another card to reveal a new image. Figure 4 provides a schematic representation of the trial sequence within an experimental block. Figure 4. Schematic representation of a trail block from Task 10 Memory for facial expression of emotion. Following the practice block, there were four experimental blocks of trials. Expression matrices included three one block , six one block , and nine two blocks pairs of expressions that were distributed pseudo-randomized across the lines and columns. Presentation time for learning depended on the memory set size: Within each block each image pair was used only once, resulting in 27 responses, representing the total number of trials for this task. The average proportion of correctly identified emotion pairs and reliability estimates, are summarized in Table 3. Similar to Task 9, guessing probability is much lower than 0. Reliability is also good. Due to the low number of trials within one emotion category, these reliabilities are rather poor, but could be increased by including additional trials. Pairs of happiness, surprise, anger, and fear expressions were remembered the best and sadness was remembered the worst. In the current version, we recommend the overall score as a psychometrically suitable performance indicator of memory for emotional expressions. We also developed speed indicators of emotion perception and emotion recognition ability following the same rationale as described by Herzmann et al. Tasks that are so easy that the measured accuracy levels are at ceiling allow us to gather individual differences in performance speed. Therefore, for the following tasks we used stimuli with high intensity prototypical expressions for which we expected recognition accuracy rates to be at or close to ceiling above 0. Like the accuracy tasks described above, the speed tasks were intended to measure either emotion perception three tasks or emotion recognition three tasks. Below we describe the six speed tasks and report results on their psychometric properties. Recognizing expressions from different viewpoints is a crucial socio-emotional competence relevant for everyday interaction. Here, we aimed to assess the speed of perceiving emotion expressions from different viewpoints by using a discrimination task with same-different choices. Two same-sex images with different facial identities were presented next to each other. One face was shown with a frontal view and the other in three-quarter view. Both displayed one of the six prototypical emotion expressions. Participants were asked to decide as fast and accurately as possible whether the two persons showed the same or different emotion expressions by pressing one of two marked keys on the keyboard. There was no time limit on the presentation. Participants' response started the next trial, after the presentation of a 1. Trials were pseudo-randomized in sequence and were balanced for expression match vs. To ensure high accuracy rates, confusable expressions according to the hexagon model Sprengelmeyer et al. There was a practice block of six trials with feedback. There were 31 experimental trials. Each of the six basic emotions occurred in match and mismatch trials. Average accuracies and RTs, along with average inverted latency see general description of scoring procedures above are presented in Table 4. As required for speed tasks, accuracy rates were at ceiling. RTs and inverted latencies showed that participants needed about two seconds on average to correctly match the two facial expressions presented in the frontal vs. Bonferroni-adjusted pairwise comparisons indicate the strongest difference in performance between matching emotion expressions occurred between happiness compared to all other emotions. Other statistically significant, but small effects indicated that performance matching surprise, fear, and anger expressions was faster than performance matching sadness and disgust. Reliability estimates are excellent for the overall score and acceptable for the emotion specific trials. However, happiness, surprise, and fear expressions were less frequently used in this task. Reliabilities of emotion-specific scores could be increased by using more trials in future applications. Table 4. Descriptive statistics and reliability estimates of performance speed for all speed measures of emotion perception—across all trials and for single target emotions. This task is a revision of the classic Odd-Man-Out task Frearson and Eysenck, , where several items are shown simultaneously of which one—the odd-man-out—differs from the others. Participants' task is to indicate the location of the odd-man-out. The emotion-expression version of the task—as implemented by Herzmann et al. Three faces of different identities but of the same sex , each displaying an emotion expression, were presented simultaneously in a row on the screen. The face in the center displayed the reference emotion from which either the left or right face differed in expression, whereas the remaining third face displayed the same emotion. Participants had to locate the divergent stimulus odd-man-out by pressing a key on the corresponding side. The next trial started after a 1. Again, we avoided combining highly confusable expressions of emotions in the same trial to ensure high accuracy rates Sprengelmeyer et al. Five practice trials with feedback and 30 experimental trials were administered in pseudo-randomized order. Each emotion occurred as both a target and as a distracter. Table 4 displays relevant results for this task. Throughout, accuracy rates were very high for all performance indicators, demonstrating the task to be a measure of performance speed. On average, participants needed about 2 s to detect the odd-man-out. Differences mainly occurred between happiness and all other expressions. In spite of the small number of trials per emotion category 5 , reliability estimates of the overall score based on inverted latencies are excellent and good for all emotion specific scores. We conclude that the overall task and emotion specific trial scores have good psychometric quality. The purpose of this task is to measure the speed of the visual search process see Task 3 involved in identifying an expression belonging to an indicated expression category. Here, an emotion label, a targeted emotional expression, and three mismatching alternative expressions, were presented simultaneously on the screen. The number of distracters was low in order to minimize task difficulty. Successful performance on this task requires a correct link of the emotion label and the facially expressed emotion and an accurate categorization of the expression to the appropriate semantic category. The name of one of the six basic emotions was printed in the center of the screen. The emotion label was surrounded in horizontal and vertical directions by four different face identities of the same sex all displaying different emotional expressions. Participants were asked to respond with their choice by using the arrow-keys on the number block of a regular keyboard. There were two practice trials at the beginning. Then, each of the six emotions was used eight times as a target in a pseudorandom sequence of 48 experimental trials. There were no time limits for the response, but participants were instructed to be as fast and accurate as possible. The ISI was ms. Average performance, as reflected by the three relevant scores for speed indicators, are depicted in Table 4. Accuracy rates were at ceiling. Expressions of happiness and surprise were detected the fastest, followed by disgust and anger, and finally sadness and fear. Reliability estimates were excellent for the overall score and good for emotion specific performance scores. All results substantiate that the scores derived from Task 12 reflect the intended difficulty for speed tasks and have good psychometric properties. In the n -back paradigm, a series of different pictures is presented; the task is to judge whether a given picture has been presented n pictures before. It has been traditionally used to measure working memory e. The 1 -back condition requires only minimal effort on storage and processing in working memory. Therefore, with the 1 -back task using emotion expressions we aimed to assess recognition speed of emotional expressions from working memory and expected accuracy levels to be at ceiling. We administered a 1 -back task with one practice block and four experimental blocks of trials. Each experimental block consisted of a sequence of 24 different images originating from the same identity displaying all six facial emotional expressions. Participants were instructed to judge whether the emotional expression of each image was the same as the expression presented in the previous trial. The two-choice response was given with a left or right key for mismatches and matches, respectively on a standard keyboard. The next trial started after the participant provided their response, with a fixation cross presented on a blank screen for ms in between trials. Response time was not limited by the experiment. All basic emotion expressions were presented as targets in at least one experimental block. Target and distracters were presented at a ratio of 1: Table 5 summarizes the average accuracies, RTs, and inverted latencies. As expected, accuracies were at ceiling. Participants were on average able to correctly respond to more than one trial per second. Reliability estimates were excellent for the overall task and acceptable for emotion specific latency scores given the low number of trials for an emotion category. These results suggest that Task 14 is a psychometrically sound measure of emotion recognition speed from faces. Table 5. Mean accuracy, reaction times in ms and reliability estimates of performance speed for all speed measures of emotion memory—across all trials and for single target emotions if applicable. The present task was inspired by the Delayed Non-Matching paradigm implemented for face identity recognition by Herzmann et al. This task requires the participant to store and maintain a memory of each emotion expression; the images are presented during the learning phase for a short period of time and during the experimental trials the images have to be recollected from the visual primary memory and compared with a novel facial expression. Because the task requires a short maintenance time for a single item in the absence of interfering stimuli, we expect the task to show accuracy rates at ceiling and to measure short-term recognition speed. A facial expression of happiness, surprise, fear, sadness, disgust, or anger was presented for 1 second. Following a delay of 4 s ms mask; ms blank screen the same emotion expression was presented together with a different facial expression. Depending on where the new distracter expression was presented, participants had to press a left or right response-key on a standard keyboard in order to indicate the distractor facial expression. In each trial we used three different identities of the same sex. During the 36 experimental trials, expressions belonging to each emotion category had to be encoded six times. There were three practice trials. Results are summarized in Table 5. Average accuracy across participants suggests ceiling effects for recognizing emotion expressions, with the exception of sadness where recognition rates were rather low. Speed of sadness recognition should be carefully interpreted because it relies on just a few latencies associated with correct responses for many of the participants. Overall, participants correctly recognized less than one item per second and exactly one item per second in the case of happy faces. The rank order of recognition speed of the six emotions followed a pattern comparable to the pattern identified for the emotion perception accuracy tasks reported earlier. Divergent stimuli compared to happiness, surprise and fear as target expressions were identified the quickest, followed by anger, disgust, and finally sadness. Reliability estimates are again excellent for the overall score and very good for emotion specific trials, suggesting good psychometric quality. This task is an implementation of a frequently used paradigm for measuring recognition memory e. The present task is derived from the face identity task and applied for emotion processing. We used morphed emotion expressions resulting in combinations of happiness, surprise, fear, sadness, disgust, or anger that were designed to appear as naturalistic as possible. Because the stimuli do not display prototypical expressions, the emotional expressions are difficult to memorize purely on the basis of semantic encoding strategies. The goal of this task was to measure visual encoding and recognition of facial expressions. In order to keep the memory demand low and to design the task to be a proper measure of speed, single expressions were presented for a relatively long period during the learning phase. This task consisted of one practice block and six experimental blocks. We kept stimulus identity constant within blocks. A block started with a 4-s learning phase, followed by a short delay during which participants were asked to answer two questions from a scale measuring extraversion, and finally the recognition phase. The extraversion items were included as an intermediate task in order to introduce a memory consolidation phase. There was one morphed expression to memorize per block during the 4-s learning time. The morphs were generated as a blend of two equally weighted, easily confusable emotional expressions according to their proximity on the emotion hexagon Sprengelmeyer et al. During retrieval, the identically morphed expression was presented three times within a pseudo-randomized sequence with three different distracters. All stimuli were presented in isolation during the recognition phase, each requiring a response. Participants indicated via a key press whether or not the presented stimulus was included in the learning phase at the beginning of the block. There were no restrictions on response time. Average performance, in terms of accuracy and the two different speed scores, are presented in Table 5. As expected, accuracy rates were at ceiling and participants were able to respond correctly to somewhat more than one trial per second on average. Reliabilities were excellent for the overall score of inverted latencies and in the acceptable range for emotion-specific trials. The indicators derived from this task are therefore suitable measures of the speed of emotion recognition from faces. We begin this discussion by providing a summary and evaluation of key findings and continue with methodological considerations regarding the overarching goal of this paper. We conclude with delineating some prospective research questions. We designed and assessed 16 tasks developed to measure individual differences in the ability to perceive or recognize facial emotion expressions. Each task explicitly measures these abilities by provoking maximum effort in participants and each item in each task has a veridical response. Performance is assessed by focusing on either the accuracy or speed of response. Competing approaches to scoring the measures were considered and compared for several tasks. Therefore, all tasks can be considered to be measures of abilities. For each of the tasks we presented emotion-specific where applicable and overall scores concerning mean performance, individual differences in performance, and precision. Additionally, coefficients of internal consistency and factor saturation were presented for each task—including emotion-specific results when possible. Taken together, the 16 tasks worked well: They were neither too easy nor too hard for the participants, and internal consistency and factor saturation were satisfactory. With respect to mean performance across all emotion domains and tasks there was an advantage for happy faces in comparison to all other facial expressions. This finding parallels several previous reports of within- and across-subject studies on facial expression recognition e. With respect to results concerning the covariance structure it might be argued that some of the emotion-specific results are not promising enough because some of the psychometric results are still in the lower range of desirable magnitudes. However, the tasks presented here ought not to be considered as stand-alone measures. Instead, preferably a compilation of these tasks should be jointly used to measure important facets of emotion-related interpersonal abilities. Methodologically, the tasks presented here would thus serve like the items of a conventional test as indicators below presupposed latent factors. Additionally, some of the unsatisfactory psychometric coefficients are likely to improve if test length is increased. Depending on available resources in a given study or application context and in line with the measurement intention tasks for one or more ability domains can be sampled from the present collection. We recommend sampling three or more tasks per ability domain. The duration estimates provided in Table 1 facilitate compilation of such task batteries in line with pragmatic needs of a given study or application context. In this paper, we presented a variety of tasks for the purpose of capturing individual differences in emotion perception and emotion recognition. The strategy in developing the present set of tasks was to sample measures established in experimental psychology and to adapt them for psychometric purposes. It is important to note that the predominant conceptual model in individual differences psychology presupposes effect indicators of common constructs. In these models, individual differences in indicators are caused by individual differences in at least one latent variable. Specific indicators in such models can be conceived as being sampled from a domain or range of tasks. Research relying on single indicators sample just one task from this domain and are therefore analogous to single case studies sampling just a single person. The virtue of sampling more than a single task is that further analysis of a variety of such measures allows abstracting not only from measurement error but also from task specificity. In elaboration of this sampling concept we defined the domain from which we were sampling tasks a priori. Although general principles of sampling tasks from a domain have been specified, implicitly by Campbell and Fiske and Cattell and more explicitly by Little et al. In this work, the authors presented a complete system for the Emotion Recognition in the Wild EmotiW Challenge [ 52 ], and proved that a hybrid CNN-RNN architecture for a facial expression analysis can outperform a previously applied CNN approach using temporal averaging for aggregation. Kim et al. The spatial image characteristics of the representative expression-state frames are learned using a CNN. In the second part, temporal characteristics of the spatial feature representation in the first part are learned using an LSTM of the facial expression. Chu et al. First, the spatial representations are extracted using a CNN, which is able to reduce person-specific biases caused by handcrafted descriptors e. To model the temporal dependencies, LSTMs are stacked on top of these representations, regardless of the lengths of the input video sequences. Hasani and Mahoor [ 54 ] proposed the 3D Inception-ResNet architecture followed by an LSTM unit that together extracts the spatial relations and temporal relations within the facial images between different frames in a video sequence. Facial landmark points are also used as inputs of this network, emphasizing the importance of facial components rather than facial regions, which may not contribute significantly to generating facial expressions. Graves et al. Jain et al. Initially, this approach subtracts the background and isolates the foreground from the images, and then extracts the texture patterns and the relevant key features of the facial points. The relevant features are then selectively extracted, and an LSTM-CNN is employed to predict the required label for the facial expressions. Commonly, deep learning-based approaches determine features and classifiers by deep neural networks experts, unlike conventional approaches. Deep learning-based approaches extract optimal features with the desired characteristics directly from data using deep convolutional neural networks. However, it is not easy to collect a large amount of training data for the facial emotion under the different conditions enough to learn deep neural networks. Moreover, deep learning-based approaches require more a higher-level and massive computing device than convention approaches to operate training and testing [ 35 ]. Therefore, it is necessary to reduce the computational burden at inference time of deep learning algorithm. Spatial image characteristics of the representative expression-state frames are learned using a CNN. Temporal characteristics of the spatial feature representation in the first part are learned using an LSTM. LSTM unit that together extracts the spatial relations and temporal relations within facial images. Using a recurrent network for temporal dependencies present in the image sequences during classification. Extraction of the texture patterns and the relevant key features of the facial points. Therefore, this hybrid model can learn to recognize and synthesize temporal dynamics for tasks involving sequential images. As shown in Figure 5 , each visual feature determined through a CNN is passed to the corresponding LSTM, and produces a fixed or variable-length vector representation. The outputs are then passed into a recurrent sequence-learning module. Finally, the predicted distribution is computed by applying softmax [ 51 , 53 ]. Overview of the general hybrid deep-learning framework for FER. In the field of FER, numerous databases have been used for comparative and extensive experiments. Traditionally, human facial emotions have been studied using either 2D static images or 2D video sequences. A 2D-based analysis has difficulty handling large pose variations and subtle facial behaviors. The analysis of 3D facial emotions will facilitate an examination of the fine structural changes inherent in spontaneous expressions [ 40 ]. Therefore, this sub-section briefly introduces some popular databases related to FER consisting of 2D and 3D video sequences and still images:. The age range of its subjects is from 18 to 30 years, most of who are female. Image sequences may be analyzed for both action units and prototypic emotions. It provides protocols and baseline results for facial feature tracking, AUs, and emotion recognition. Compound Emotion CE [ 17 ]: CE contains images corresponding to 22 categories of basic and compound emotions for its human subjects females and males, mean age of Most ethnicities and races are included, including Caucasian, Asian, African, and Hispanic. Facial occlusions are minimized, with no glasses or facial hair. Male subjects were asked to shave their faces as cleanly as possible, and all participants were also asked to uncover their forehead to fully show their eyebrows. The database also includes 66 facial landmark points for each image in the database. It was designed for research on 3D human faces and facial expressions, and for the development of a general understanding of human behavior. It contains a total of subjects, 56 females and 44 males, displaying six emotions. There are 25 3D facial emotion models per subject in the database, and a set of 83 manually annotated facial landmarks associated with each model. The JAFFE database contains images of seven facial emotions six basic facial emotions and one neutral posed by ten different female Japanese models. Each image was rated based on six emotional adjectives using 60 Japanese subjects. This database consists of a set of 16, facial images taken under a single light source, and contains 28 distinct subjects for viewing conditions, including nine poses for each of 64 illumination conditions. MMI [ 43 ]: MMI consists of over video sequences and high-resolution still images of 75 subjects. It is fully annotated for the presence of AUs in the video sequences event coding , and partially coded at the frame-level, indicating for each frame whether an AU is in a neutral, onset, apex, or offset phase. It contains a total of video sequences on 28 subjects, both males and females. BP4D-spontanous is a 3D video database that includes a diverse group of 41 young adults 23 women, 18 men with spontaneous facial expressions. The subjects were 18—29 years in age. The facial features were tracked in the 2D and 3D domains using both person-specific and generic approaches. The database promotes the exploration of 3D spatiotemporal features during subtle facial expressions for a better understanding of the relation between pose and motion dynamics in facial AUs, as well as a deeper understanding of naturally occurring facial actions. This database contains images of human emotional facial expressions. The database consists of 70 individuals, each displaying seven different emotional expressions photographed from five different angles. Table 4 shows a summary of these publicly available databases. Provides protocols and baseline results for facial feature tracking, action units, and emotion recognition. Examples of nine representative databases related to FER. Databases a through g support 2D still images and 2D video sequences, and databases h through i support 3D video sequences. Unlike the databases described above, MPI facial expression database [ 60 ] collects a large variety of natural emotional and conversational expressions under the assumption that people understand emotions by analyzing both the conversational expressions as well as the emotional expressions. This database consists of more than 18, samples of video sequences from 10 females and nine male models displaying various facial expressions recorded from one frontal and two lateral views. In recent, other sensors, such as NIR camera, thermal camera, and Kinect sensors, are having interesting of FER researches because visible light image is easily changeable when there are changes in environmental illumination conditions. Natural visible and infrared facial expression USTC-NVIE database [ 32 ] collected both spontaneous and posed expressions of more than subjects simultaneously using a visible and an infrared thermal camera. Facial expressions and emotions database FEEDB is a multimodal database of facial expressions and emotion recorded using Microsoft Kinect sensor. It contains of recordings of 50 persons posing for 33 different facial expressions and emotions [ 33 ]. As described here, various sensors other than the camera sensor are used for FER, but there is a limitation in improving the recognition performance with only one sensor. Therefore, it is predicted that the attempts to increase the FER through the combination of various sensors, will continue in the future. Given the FER approaches, evaluation metrics of the FER approaches are crucial because they provide a standard for a quantitative comparison. In this section, a brief review of publicly available evaluation metrics and a comparison with the benchmark results are provided. Many approaches are used to evaluate the accuracy using two different experiment protocols: First, a subject-independent task splits each database into training and validation sets in a strict subject-independent manner. This task is also called a K-fold cross-validation. The purpose of K-fold cross-validation is to limit problems such as overfitting and provide insight regarding how the model will generalize into an independent unknown dataset [ 61 ]. With the K-fold cross-validation technique, each dataset is evenly partitioned into K folds with exclusive subjects. Then, a model is iteratively trained using K-1 folds and evaluated on the remaining fold, until all subjects are tested. The accuracy is estimated by averaging the recognition rate over K folds. For example, in ten-fold cross-validation adopted for an evaluation, nine folds are used for training, and one fold is used for testing. After this process is performed ten different times, the accuracies of the ten results are averaged and defined as the classifier performance. The second protocol is a cross-database task. In this task, one dataset is used entirely for testing the model, and the remaining datasets listed in Table 4 are used to train the model. The model is iteratively trained using K-1 datasets and evaluated on the remaining dataset repeatedly until all datasets have been tested. The accuracy is estimated by averaging the recognition rate over K datasets in a manner similar to K-fold cross-validation. The evaluation metrics of FER are classified into four methods using different attributes: The precision is the fraction of automatic annotations of emotion i that are correctly recognized. The recall is the number of correct recognitions of emotion i over the actual number of images with emotion i [ 18 ]. The accuracy is the ratio of true outcomes both true positive to true negative to the total number of cases examined. Another metric, the F1-score, is divided into two metrics depending on whether they use spatial or temporal data: Each metric captures different properties of the results. This means that a frame-based F-score has predictive power in terms of spatial consistency, whereas an event-based F-score has predictive power in terms of the temporal consistency [ 62 ]. A frame-based F1-score is defined as. An event-based F1-score is used to measure the emotion recognition performance at the segment level because emotions occur as a temporal signal. ER is the ratio of correctly detected events over the true events, while the EP is the ratio of correctly detected events over the detected events. F1-event considers that there is an event agreement if the overlap is above a certain threshold [ 63 ]. To show a direct comparison between conventional handcrafted-feature-based approaches and deep-learning-based approaches, this review lists public results on the MMI dataset. Table 5 shows the comparative recognition rate of six conventional approaches and six deep-learning-based approaches. Recognition performance with MMI dataset, adapted from [ 11 ]. Sparse representation classifier with LBP features [ 63 ]. Sparse representation classifier with local phase quantization features [ 64 ]. SVM with Gabor wavelet features [ 65 ]. Sparse representation classifier with LBP from three orthogonal planes [ 66 ]. Sparse representation classifier with local phase quantization feature from three orthogonal planes [ 67 ]. Collaborative expression representation CER [ 68 ]. Deep learning of deformable facial action parts [ 69 ]. Joint fine-tuning in deep neural networks [ 48 ]. AU-aware deep networks [ 70 ]. AU-inspired deep networks [ 71 ]. Deeper CNN [ 72 ]. As shown in Table 5 , deep-learning-based approaches outperform conventional approaches with an average of In conventional FER approaches, the reference [ 68 ] has the highest performance than other algorithms. Because the feature extraction is robust to face rotation and misalignment, this study achieves relatively accurate FER than other conventional methods. Among several deep-learning-based approaches, two have a relatively higher performance compared to several state-of-the-art methods; a complex CNN network proposed in [ 72 ] consists of two convolutional layers, each followed by max pooling and four Inception layers. This network has a single-component architecture that takes registered facial images as the input and classifies them into one of six basic or one neutral expression. United States: McGraw-Hill, A Contribution to the Ontogenesis of Social Relations. American Sign Language: A teacher's resource text on grammar and culture. Silver Spring, MD: The linguistics of British Sign Language. Friesen; P. Ellsworth Guidelines for research and a review of findings". New York: Experimental Psychology. Henry Holt. A review of the cross-cultural studies". Emotion Review. Proceedings of the National Academy of Sciences. Friesen Journal of Personality and Social Psychology. Facial Action Coding System 3. Manual of Scientific Codification of the Human Face. Russell Judging emotion from the face in context". September Journal of Nonverbal Behavior. Darwin and facial expression: Cambridge, MA: Malor Books. CS1 maint: Extra text: Physiology of behavior 11th ed. New Jersey: Pearson Education Inc. Multiple names: A Review of the Cross-Cultural Studies". Authority control GND: Retrieved from " https: Facial expressions Anatomical simulation Emotion. Hidden categories: Namespaces Article Talk. Views Read Edit View history. In other projects Wikimedia Commons Wikiquote. Improvements may also be possible if it were to better understand how facial expressions of emotion affect these people. Other syndromes such as autism are also of great importance these days. More children than ever are being diagnosed with the disorder CDC, ; Prior, We know that autistic children do not perceive facial expressions of emotion as others do Jemel et al. A modified computational model of the perception of facial expressions of emotion in autism could help design better teaching tools for this group and may bring us closer to understanding the syndrome. There are indeed many great possibilities for machine learning researchers to help move these studies forward. Extending or modifying the modeled summarized in the present paper is one way. Developing machine learning algorithms to detect face landmark more accurately is another. Developing statistical tools that more accurately represent the underlying manifold or distribution of the data is yet another great way to move the state of the art forward. In the present work we have summarized the development of a model of the perception of facial expressions of emotion by humans. A key idea in this model is to linearly combine a set of face spaces defining some basic emotion categories. The model is consistent with our current understanding of human perception and can be successfully exploited to achieve great recognition results for computer vision and HCI applications. We have shown how, to be consistent with the literature, the dimensions of these computational spaces need to encode configural and shape features. We conclude that to move the state of the art forward, face recognition research has to focus on a topic that has received little attention in recent years—precise, detailed detection of faces and facial features. Although we have focused our study on the recognition of facial expressions of emotion, we believe that the results apply to most face recognition tasks. We have listed a variety of ways in which the machine learning community can get involved in this research project and briefly discussed applications in the study of human perception and the better understanding of disorders. J Mach Learn Res. Author manuscript; available in PMC Aug Aleix Martinez and Shichuan Du. Aleix Martinez: Copyright notice. The publisher's final edited version of this article is available at J Mach Learn Res. See other articles in PMC that cite the published article. Abstract In cognitive science and neuroscience, there have been two leading models describing how humans perceive and classify facial expressions of emotion—the continuous and the categorical model. Introduction The face is an object of major importance in our daily lives. Facial Expressions: From Production to Perception The human face is an engineering marvel. Open in a separate window. Figure 1. A Model of the Perception of Facial Expressions of Emotion In cognitive science and neuroscience researchers have been mostly concerned with models of the perception and classification of the six facial expressions of emotion listed above. Figure 2. Figure 3. Dimensions of the Model In the early years of computer vision, researchers derived several feature- and shape-based algorithms for the recognition of objects and faces Kanade, ; Marr, ; Lowe, Figure 4. Figure 5. Figure 6. Precise Detection of Faces and Facial Features As seen thus far, human perception is extremely tuned to small configural and shape changes. Figure 7. Two example of imprecise detections of a face with a state of the art algorithm. Figure 8. Figure 9. Figure Discussion In the real world, occlusions and unavoidable imprecise detections of the fiducial points, among others, are known to affect recognition Torre and Cohn, ; Martinez, Conclusions In the present work we have summarized the development of a model of the perception of facial expressions of emotion by humans. Inversion and configuration of faces. Cognitive Psychology. Categorical effects in the perception of faces. Face recognition: Computer-enhanced emotion in facial expressions. Proceedings of the Royal Society of London B. Neuropsychology of fear and loathing. Nature Review Neuroscience. Understanding emotions from standardized facial expressions in autism and normal development. Center for Disease Control and Prevention. Prevalence of autism spectrum disorders autism and developmental disabilities monitoring network, 14 sites, united states, Active appearance models. Emotion, Reason, and the Human Brain. The Expression of the Emotions in Man and Animal. Murray; London: Features versus context: An approach for precise and detailed detection and delineation of faces and facial features. The resolution of facial expressions of emotion. Journal of Vision. Pictures of Facial Affect. What the Face Reveals: Oxford University Press; New York: Computing smooth time-trajectories for camera and deformable shape in structure from motion with occlusion. Kernel non-rigid structure from motion. Bayes optimality in linear discriminant analysis. Rotation invariant kernels and their application to shape analysis. Active appearance models with rotation invariant kernels. IEEE Proc. International Conference on Computer Vision; b. The effect of feature displacement on the perception of well-known faces. Emotion theory and research: Highlights, unanswered questions, and emerging issues. Annual Review of Psychology. Impaired face processing in autism: Fact or artifact? Journal of Autism and Developmental Disorders. PhD thesis. Kyoto University; Japan: Probabilistic non-linear principal component analysis with gaussian process latent variable models. Journal of Machine Learning Research. Emotion circuits in the brain. Annual Review of Nueroscience. Three-dimensional object recognition from single two-dimensional images. Artificial Intelligence. Philosophical Transactions of the Royal Society of London. Recognizing imprecisely localized, partially occluded and expression variant faces from a single sample per class. Matching expression variant faces. Vision Research. Deciphering the face. IEEE Conf. Computer Vision and Pattern Recognition, workshop; The Society of Mind. Emotion perception in emotionless face images suggests a norm-based representation. A computational shape-based model of anger and sadness justifies a configural representation of faces. Looking at people: Sensing for ubiquitous and wearable computing. Is there an increase in the prevalence of autism spectrum disorders? Journal of Paediatrics and Child Health. Identification and ratings of caricatures: Learning shape manifolds. Pattern Recognition. A theory of emotion, and its application to understanding the neural basis of emotion. Cognition and Emotion. A circumplex model of affect. J Personality Social Psych. Core affect and the psychological construction of emotion. Psychological Review. Human facial expressions as adaptations: Evolutionary questions in facial expression. Yearbook of Physical Anthropology. Low-dimensional procedure for the characterization of human faces. J Optical Soc Am A..

Learning why some expressions are so readily classified by our visual system should article source the definition of the form and dimensions of the computational model of facial expressions of emotion. The search is on to resolve these two problems. First, we need to determine the form of the computational space e. Second, we ought to define the dimensions of this model e. In the following sections we overview the research check this out have conducted in the last several years leading to a solution to the above questions.

We then discuss on the implications of this model. In particular, we provide a perspective on how machine learning and computer vision researcher should move Category emotion expression face facial if they are to define models based on the perception of facial expressions of emotion by humans.

In cognitive science and neuroscience researchers have been mostly concerned with models of the perception and classification of the six facial expressions of emotion listed above.

Similarly, computer vision and machine learning algorithms generally employ a face space to represent these six emotions. Sample feature vectors or regions of this feature space are used to represent each of these six emotion labels. This approach has a major drawback—it can only detect one emotion from a single image. In machine learning, this is generally done by a winner-takes-all approach Torre and Cohn, This means that when a new category wants to be included, one generally needs to provide labeled samples of it to the learning algorithm.

Yet, everyday experience demonstrates that we can perceive more than one emotional category in a single image Martinez,even if we have no prior experience with it. For example, Figure 2 shows images of faces expressing different surprises—happily surprised, angrily surprised, fearfully surprised, disgustedly surprised and the typically studied surprise. Faces expressing different surprise. From left to right: If we were to use a continuous model, we would need to have a Category emotion expression face facial large number of Category emotion expression face facial represented all over the space; including all possible types of surprises.

This would require a very large training set, since each possible combination of labels would have to be learned. But this is the same problem a categorical model Category emotion expression face facial face. In such a case, dozens if not hundreds of sample images for each possible category would be needed. Alternatively, Susskind et al. If we define an independent computational face space for a small number of emotion labels, we will only need sample faces of those few facial expressions of emotion.

This is indeed the approach we have taken. Details of this model are given next. Key to this model is to note that we can define new categories as linear combinations of a small set of categories. Figure 3 illustrates this approach. In this figure, we show how we can obtain the above listed different surprises as a linear combination of known categories.

A large number of such expressions exist that are a combination of the six emotion categories listed above and, hence, the above list of six categories is a potential set of basic emotion classes.

Also, there is some evidence form cognitive science to suggest that these are important categories for humans Izard, Of course, one needs not base the model on this set of six emotions. This is an area that will undoubtedly attract lots of interest. A question of particular interest is to determine not only which basic categories to include in the model but how many.

To this end both, cognitive studies with humans and computational extensions of the proposed model will be necessary, with the results of one area aiding the research of the other. This figure shows how to construct Category emotion expression face facial combinations of known categories. At the top of the figure, we have the known or learned categories emotions. The coefficients s i determine the contribution of each of these categories to the final perception of the emotion.

The approach described in the preceding paragraph would correspond to a categorical model. However, we now go one step further and define each of these face spaces as continuous feature spaces, Figure 3. This allows for the perception of each emotion at different intensities, for example, less happy to exhilarant Neth and Martinez, Less happy would correspond to a feature vector in the left most face space in the figure closer to the mean or origin of the feature space.

Feature vectors farther from the mean would be perceived as happier. The proposed model also explains the caricature effect, because within each category the face space is continuous and exaggerating the expression will move the feature vector representing the expression further from the mean of that category.

In essence, the intensity observed in this continuous representation defines the weight of the contribution of each basic category toward the final decision classification.

It also allows for the representation and recognition of a very large number of emotion categories without the need to have a categorical space for each or having to use many samples of each expression as Category emotion expression face facial the continuous model. The proposed model thus bridges the gap between the categorical and continuous ones and resolves Category emotion expression face facial of the debate facing each of the models individually.

To complete the definition of the model, we need to specify what defines each of the dimensions of the continuous spaces representing each category. We turn to this problem in the next Category emotion expression face facial. In the early years of computer vision, researchers derived several feature- and shape-based algorithms for the recognition of objects and faces Kanade, ; Marr, ; Lowe, Interaction Design and Usability; Beijing, China.

Hickson S. Classifying facial expressions in VR using eye-tracking cameras. Chen Category emotion expression face facial. Augmented reality-based self-facial modeling to promote the emotional expression and social skills of adolescents Category emotion expression face facial autism spectrum disorders. Assari M. Zhan C. A real-time facial expression recognition system for online games.

Games Technol. Competitive affective gaming: Lucey P. Kahou S. Walecki R. Deep structured learning for facial expression intensity estimation.

Image Vis. Kim D. Multi-objective based Spatio-temporal feature representation learning robust to expression intensity variations for facial expression recognition. Mg midget austin healey Trans. Ekman P. Facial Action Coding System: Hamm J. Automated facial action coding system for dynamic analysis of facial expressions in neuropsychiatric disorders.

Jeong M. Driver facial landmark detection in real driving situation. Circuits Syst. Video Technol. Category emotion expression face facial S. Compound facial expressions of emotion. Benitez-Quiroz C. Category emotion expression face facial A.

A review of emotion recognition methods based on keystroke dynamics and mouse movements; Proceedings of the 6th International Conference on Human System Interaction; Gdansk, Poland. Kumar S. Facial expression recognition: Ghayoumi M. A quick review Category emotion expression face facial deep learning in facial expression. Suk M. Ghimire D. Geometric feature-based facial Category emotion expression face facial recognition in image sequences using multi-class AdaBoost and support vector machines.

Happy S. A real time facial expression classification system using local binary patterns; Proceedings of the 4th International Conference on Intelligent Human Computer Interaction; Kharagpur, India. Siddiqi M. Human facial expression recognition using stepwise linear discriminant analysis and hidden conditional random fields.

Image Proc. Khan R. Framework for reliable, real-time facial expression recognition for Continue reading resolution images. Pattern Recognit. Facial expression recognition based on local region specific features and support vector machines. Tools Appl. Torre F. Polikovsky S. Sandbach G. Static and dynamic 3D Category emotion expression face facial expression recognition: A comprehensive survey.

Zhao G. Facial expression recognition from near-infrared videos. Shen P. Facial expression recognition from infrared thermal videos.

Category emotion expression face facial

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Xxxnx Stars Watch Porn Movies Annuska Sex. Finally, the predicted distribution is computed by applying softmax [ 51 , 53 ]. Overview of the general hybrid deep-learning framework for FER. In the field of FER, numerous databases have been used for comparative and extensive experiments. Traditionally, human facial emotions have been studied using either 2D static images or 2D video sequences. A 2D-based analysis has difficulty handling large pose variations and subtle facial behaviors. The analysis of 3D facial emotions will facilitate an examination of the fine structural changes inherent in spontaneous expressions [ 40 ]. Therefore, this sub-section briefly introduces some popular databases related to FER consisting of 2D and 3D video sequences and still images:. The age range of its subjects is from 18 to 30 years, most of who are female. Image sequences may be analyzed for both action units and prototypic emotions. It provides protocols and baseline results for facial feature tracking, AUs, and emotion recognition. Compound Emotion CE [ 17 ]: CE contains images corresponding to 22 categories of basic and compound emotions for its human subjects females and males, mean age of Most ethnicities and races are included, including Caucasian, Asian, African, and Hispanic. Facial occlusions are minimized, with no glasses or facial hair. Male subjects were asked to shave their faces as cleanly as possible, and all participants were also asked to uncover their forehead to fully show their eyebrows. The database also includes 66 facial landmark points for each image in the database. It was designed for research on 3D human faces and facial expressions, and for the development of a general understanding of human behavior. It contains a total of subjects, 56 females and 44 males, displaying six emotions. There are 25 3D facial emotion models per subject in the database, and a set of 83 manually annotated facial landmarks associated with each model. The JAFFE database contains images of seven facial emotions six basic facial emotions and one neutral posed by ten different female Japanese models. Each image was rated based on six emotional adjectives using 60 Japanese subjects. This database consists of a set of 16, facial images taken under a single light source, and contains 28 distinct subjects for viewing conditions, including nine poses for each of 64 illumination conditions. MMI [ 43 ]: MMI consists of over video sequences and high-resolution still images of 75 subjects. It is fully annotated for the presence of AUs in the video sequences event coding , and partially coded at the frame-level, indicating for each frame whether an AU is in a neutral, onset, apex, or offset phase. It contains a total of video sequences on 28 subjects, both males and females. BP4D-spontanous is a 3D video database that includes a diverse group of 41 young adults 23 women, 18 men with spontaneous facial expressions. The subjects were 18—29 years in age. The facial features were tracked in the 2D and 3D domains using both person-specific and generic approaches. The database promotes the exploration of 3D spatiotemporal features during subtle facial expressions for a better understanding of the relation between pose and motion dynamics in facial AUs, as well as a deeper understanding of naturally occurring facial actions. This database contains images of human emotional facial expressions. The database consists of 70 individuals, each displaying seven different emotional expressions photographed from five different angles. Table 4 shows a summary of these publicly available databases. Provides protocols and baseline results for facial feature tracking, action units, and emotion recognition. Examples of nine representative databases related to FER. Databases a through g support 2D still images and 2D video sequences, and databases h through i support 3D video sequences. Unlike the databases described above, MPI facial expression database [ 60 ] collects a large variety of natural emotional and conversational expressions under the assumption that people understand emotions by analyzing both the conversational expressions as well as the emotional expressions. This database consists of more than 18, samples of video sequences from 10 females and nine male models displaying various facial expressions recorded from one frontal and two lateral views. In recent, other sensors, such as NIR camera, thermal camera, and Kinect sensors, are having interesting of FER researches because visible light image is easily changeable when there are changes in environmental illumination conditions. Natural visible and infrared facial expression USTC-NVIE database [ 32 ] collected both spontaneous and posed expressions of more than subjects simultaneously using a visible and an infrared thermal camera. Facial expressions and emotions database FEEDB is a multimodal database of facial expressions and emotion recorded using Microsoft Kinect sensor. It contains of recordings of 50 persons posing for 33 different facial expressions and emotions [ 33 ]. As described here, various sensors other than the camera sensor are used for FER, but there is a limitation in improving the recognition performance with only one sensor. Therefore, it is predicted that the attempts to increase the FER through the combination of various sensors, will continue in the future. Given the FER approaches, evaluation metrics of the FER approaches are crucial because they provide a standard for a quantitative comparison. In this section, a brief review of publicly available evaluation metrics and a comparison with the benchmark results are provided. Many approaches are used to evaluate the accuracy using two different experiment protocols: First, a subject-independent task splits each database into training and validation sets in a strict subject-independent manner. This task is also called a K-fold cross-validation. The purpose of K-fold cross-validation is to limit problems such as overfitting and provide insight regarding how the model will generalize into an independent unknown dataset [ 61 ]. With the K-fold cross-validation technique, each dataset is evenly partitioned into K folds with exclusive subjects. Then, a model is iteratively trained using K-1 folds and evaluated on the remaining fold, until all subjects are tested. The accuracy is estimated by averaging the recognition rate over K folds. For example, in ten-fold cross-validation adopted for an evaluation, nine folds are used for training, and one fold is used for testing. After this process is performed ten different times, the accuracies of the ten results are averaged and defined as the classifier performance. The second protocol is a cross-database task. In this task, one dataset is used entirely for testing the model, and the remaining datasets listed in Table 4 are used to train the model. The model is iteratively trained using K-1 datasets and evaluated on the remaining dataset repeatedly until all datasets have been tested. The accuracy is estimated by averaging the recognition rate over K datasets in a manner similar to K-fold cross-validation. The evaluation metrics of FER are classified into four methods using different attributes: The precision is the fraction of automatic annotations of emotion i that are correctly recognized. The recall is the number of correct recognitions of emotion i over the actual number of images with emotion i [ 18 ]. The accuracy is the ratio of true outcomes both true positive to true negative to the total number of cases examined. Another metric, the F1-score, is divided into two metrics depending on whether they use spatial or temporal data: Each metric captures different properties of the results. This means that a frame-based F-score has predictive power in terms of spatial consistency, whereas an event-based F-score has predictive power in terms of the temporal consistency [ 62 ]. A frame-based F1-score is defined as. An event-based F1-score is used to measure the emotion recognition performance at the segment level because emotions occur as a temporal signal. ER is the ratio of correctly detected events over the true events, while the EP is the ratio of correctly detected events over the detected events. F1-event considers that there is an event agreement if the overlap is above a certain threshold [ 63 ]. To show a direct comparison between conventional handcrafted-feature-based approaches and deep-learning-based approaches, this review lists public results on the MMI dataset. Table 5 shows the comparative recognition rate of six conventional approaches and six deep-learning-based approaches. Recognition performance with MMI dataset, adapted from [ 11 ]. Sparse representation classifier with LBP features [ 63 ]. Sparse representation classifier with local phase quantization features [ 64 ]. SVM with Gabor wavelet features [ 65 ]. Sparse representation classifier with LBP from three orthogonal planes [ 66 ]. Sparse representation classifier with local phase quantization feature from three orthogonal planes [ 67 ]. Collaborative expression representation CER [ 68 ]. Deep learning of deformable facial action parts [ 69 ]. Joint fine-tuning in deep neural networks [ 48 ]. AU-aware deep networks [ 70 ]. AU-inspired deep networks [ 71 ]. Deeper CNN [ 72 ]. As shown in Table 5 , deep-learning-based approaches outperform conventional approaches with an average of In conventional FER approaches, the reference [ 68 ] has the highest performance than other algorithms. Because the feature extraction is robust to face rotation and misalignment, this study achieves relatively accurate FER than other conventional methods. Among several deep-learning-based approaches, two have a relatively higher performance compared to several state-of-the-art methods; a complex CNN network proposed in [ 72 ] consists of two convolutional layers, each followed by max pooling and four Inception layers. This network has a single-component architecture that takes registered facial images as the input and classifies them into one of six basic or one neutral expression. The highest performance approach [ 13 ] also consists of two parts. In the first part, the spatial image characteristics of the representative expression-state frames are learned using a CNN. In the second part, the temporal characteristics of the spatial feature representation in the first part are learned using an LSTM of the facial expression. Based on the accuracy of a complex hybrid approach using spatio-temporal feature representation learning, the FER performance of largely affected not only by the spatial changes but also by the temporal changes. Although deep-learning-based FER approaches have achieved great success in experimental evaluations, a number of issues remain that deserve further investigation:. A large-scale dataset and massive computing power are required for training as the structure becomes increasingly deep. Large memory is demanded, and the training and testing are both time consuming. These memories demanding and computational complexities make deep learning ill-suited for deployment on mobile platforms with limited resources [ 73 ]. Considerable skill and experience are required to select suitable hyper parameters, such as the learning rate, kernel sizes of the convolutional filters, and the number of layers. These hyper-parameters have internal dependencies that make them particularly expensive for tuning. Although they work quite well for various applications, a solid theory of CNNs is still lacking, and thus users essentially do not know why or how they work. This paper presented a brief review of FER approaches. As we described, such approaches can be divided into two main streams: As a particular type of deep learning, a CNN visualizes the input images to help understand the model learned through various FER datasets, and demonstrates the capability of networks trained on emotion detection, across both the datasets and various FER related tasks. However, because CNN-based FER methods cannot reflect the temporal variations in the facial components, hybrid approaches have been proposed by combining a CNN for the spatial features of individual frames, and an LSTM for the temporal features of consecutive frames. However, deep-learning-based FER approaches still have a number of limitations, including the need for large-scale datasets, massive computing power, and large amounts of memory, and are time consuming for both the training and testing phases. Moreover, although a hybrid architecture has shown a superior performance, micro-expressions remain a challenging task to solve because they are more spontaneous and subtle facial movements that occur involuntarily. This paper also briefly introduced some popular databases related to FER consisting of both video sequences and still images. In a traditional dataset, human facial expressions have been studied using either static 2D images or 2D video sequences. However, because a 2D-based analysis has difficulty handling large variations in pose and subtle facial behaviors, recent datasets have considered 3D facial expressions to better facilitate an examination of the fine structural changes inherent to spontaneous expressions. Furthermore, evaluation metrics of FER-based approaches were introduced to provide standard metrics for comparison. Evaluation metrics have been widely evaluated in the field of recognition, and precision and recall are mainly used. However, a new evaluation method for recognizing consecutive facial expressions, or applying micro-expression recognition for moving images, should be proposed. Although studies on FER have been conducted over the past decade, in recent years the performance of FER has been significantly improved through a combination of deep-learning algorithms. Because FER is an important way to infuse emotion into machines, it is advantageous that various studies on its future application are being conducted. If emotional oriented deep-learning algorithms can be developed and combined with additional Internet-of-Things sensors in the future, it is expected that FER can improve its current recognition rate, including even spontaneous micro-expressions, to the same level as human beings. Morphed images were created from two different expressions with theoretically postulated and empirically tested maximal confusion rates Ekman and Friesen, Thus, morphs were created on the following six continua: These morphs were created for each face separately for five female and five male models. In every trial, two images of the same identity were presented on the upper left and on the upper right side of the screen, where each image displayed a different prototypical emotion expression happiness, surprise, fear, sadness, disgust, and anger. Below these faces, centered on the screen, was a single expression morphed from the prototypical faces displayed in the upper part of the screen. All three faces remained on the screen until participants responded. Participants were asked to estimate the ratio of the morphed photo on a continuous visual analog scale. Participants were asked to estimate the mixture-ratio of the morph photo as exactly as possible, using the full range of the scale. There were no time limits. Three practice trials preceded 60 experimental trials. We scored performance accuracy as the average absolute deviation of participants' response from the correct proportion of the mixture between the two parent expressions. Table 2 displays the average overall and emotion specific deviation scores. Further, it was interesting to investigate whether performance was higher toward the ends of the continua as predicted by categorical accounts of emotional expression perception. A series of two-tailed paired t -tests compared differences between the emotion categories of the parent photo. The correct mixture ratio was better identified in the following combinations: Generally, we expected mixtures of more similar expressions to bias the evaluation of the morphs. The results are essentially in line with these expectations based on expression similarities. Taken together, the results suggest the deviation scores meet psychometric standards. Performance improved or worsened as predicted by theories of categorical perception. Future research should examine whether expression assignment in morphed emotions is indicative of the ability to identify prototypical emotion expressions. This task is a forced choice version of the previously described Task 4 and aims to measure categorical perception of emotional expressions using a further assessment method. Participants were asked to decide whether the morphed expression presented in the upper middle of the screen was more similar to the expression prototype displayed on the lower left or lower right side of the screen. Stimuli were identical with those used in Task 4, but the sequence of presentation was different. The task design differed from that of Task 4 only in that participants were forced to decide whether the expression-mix stimulus was composed of more of the left or more of the right prototypical expression. Response keys were the left and right control keys on the regular computer keyboard, which were marked with colored tape. The average percentages of correct decisions are given in Table 2. This task was rather easy compared with Tasks 1—3. The distribution of the scores was, however, not strongly skewed to the right, but rather followed a normal distribution with most of the participants performing within the range of 0. Similarly to Task 4, the rank order of emotion recognizability was not similar to Tasks 1 or 2. Generally, the psychometric properties of this task need improvement and further studies should address the question whether forced-choice expression assignment in emotion-morphs is indicating the same ability factor indicated by the other tasks i. The following five tasks arguably assess individual differences in memory-related abilities in the domain of facial expressions. All tasks consist of a learning phase for facial expressions and a subsequent retrieval phase that requires recognition or recall of previously learned expressions. The first three memory tasks include an intermediate task between learning and recall of at least three minutes, hence challenging long-term retention. In Task 9 and 10, learning is immediately followed by retrieval. With this forced-choice SM task we aimed to assess the ability to learn and recognize facial expressions of different intensity. Emotion category, emotion intensity, and learning-set size varied across trials, but face identity was constant within a block of expressions that the participant was asked to learn together. Manipulations of expression intensity within targets, but also between targets and distracters, were used to increase task difficulty. The recognition of expression intensity is also a challenge in everyday life; hence, the expression intensity manipulation is not restricted to psychometric rationales. We expected hit-rates to decline with increasing ambiguity for less intense targets e. We administered one practice block of trials and four experimental blocks—including four face identities half were females and 18 trials per block. Each block started by presenting a set of target faces of the same face identity but with different emotion expressions. To-be-learned stimuli were presented simultaneously in a line centered on the screen. Experimental blocks differed in the number of targets, expressed emotion, expression intensity, and presentation time. Presentation time ranged from 30 to 60 s depending on the number of targets within a block two up to five stimuli. Facial expressions of six emotions were used as targets as well as distracters happiness, surprise, anger, fear, sadness, and disgust. Participants were instructed to remember the combination of both expression and intensity. During a delay phase of about three minutes, participants worked on a two-choice RT task they had to decide whether two simultaneous presented number series are the same or different. Recall was structured as a pseudo-randomized sequence of 18 single images of targets or distracters. Targets were identical with the previously learned expressions in terms of emotional content and intensity, but different photographs of the same identities were used in order to reduce effects of simple image recognition. Distracters differed from the targets in both expression content and intensity. Participants were requested to provide a two-choice discrimination decision between learned and distracter expressions on the keyboard. After a response, the next stimulus was presented. The average performance accuracy over all trials and across trials of specific emotion categories is presented in Table 3. Pairwise comparison based on adjusted p -values for simultaneous inference using the Bonferroni-method showed that participants were better at recognizing happiness relative to all other emotions. Additionally, expressions of anger were significantly better retrieved than surprise, fear, or disgust expressions. There were no additional performance differences due to emotion content. Table 3. Descriptive statistics and reliability estimates of performance accuracy for all emotion memory tasks—across all trials and for single target emotions if applicable. Intensity manipulation was partly successful: While performance on low intensity stimuli was slightly better than performance on medium intensity stimuli, we believe this effect reflects a Type 1 error and will not replicate in an independent sample. We recommend using high vs. Reliability estimates are provided in Table 3 and suggest good psychometric properties for the overall task. Reliabilities for emotion-specific trials are acceptable considering the low number of indicators and the heterogeneity of facial emotion expressions in general. In sum, we recommend using two levels of difficulty and the overall performance as indicators of expression recognition accuracy for this task. In this delayed recognition memory task, we displayed facial expressions with a frontal view as well as right and left three-quarter views. We aimed to assess long-term memory bindings between emotion expressions and face orientation. Thus, in order to achieve a correct response, participants needed to store both the emotion expressions and the viewpoints. This task is based on the premise that remembering content-context bindings is crucial in everyday socio-emotional interactions. An obvious hypothesis regarding this task is that emotion expressions are recognized more accurately from the frontal view than from the side, because more facial muscles are visible from the frontal view. On the other side, Matsumoto and Hwang reported that the presentation of emotional expressions in hemi-face profiles did not lower accuracy rates of recognition relative to frontal views. It is important to note that manipulation of the viewpoint is confounded with manipulating gaze direction in the present task. Adams and Kleck discuss effects of gaze direction on the processing of emotion expressions. A comparison of accuracy rates between frontal and three-quarter views is therefore interesting. This task includes one practice block and four experimental blocks with each including only one face identity and consisting of 12—16 recall-trials. During the initial learning phase, emotion expressions from different viewpoints were simultaneously presented. The memory set size varied across blocks from four to seven target stimuli. Targets differed according to the six basic emotions and the three facial perspectives frontal, left, and right profile views. Presentation time changed depending on the number of stimuli presented during the learning phase and ranged between 30 and 55 s. Participants were explicitly instructed to memorize the association between expressed emotion and perspective. These images were shown in a pseudo-randomized sequence intermixed with distracters, which differed from the targets in expression, perspective, or both. Participants were asked to decide whether or not a given image had been shown during the learning phase by pressing one of two buttons on the keyboard. After the participants chose their response, the next trial started. Table 3 displays the performance accuracy for this task. The average scores suggest adequate levels of task difficulty—well above guessing probability and below ceiling. Reliability estimates for the emotion specific trials were considerably lower; these estimates might be raised by increasing the number of stimuli per emotion category. Pairwise comparisons showed that expressions of happiness and surprise were recognized the best and anger and fear were recognized the worst. Viewpoint effects were as expected and contradict the results from Matsumoto and Hwang Expressions were recognized significantly better if the expression was learned with frontal rather than a three quarter view: We recommend using face orientation as a difficulty manipulation and the overall performance across trials as indicator of expression recognition. With this task, we intended to assess recognition performance of mixed—rather than prototypical—facial expressions. It was not our aim to test theories that postulate combinations of emotions that result in complex affect expressions, such as contempt, which is proposed as a mixture of anger and disgust or disappointment, which is proposed as a combination of surprise and sadness cf. Plutchik, Instead, we aimed to use compound emotion expressions to assess the ability to recognize less prototypical, and to some extent more real-life, expressions. Furthermore, these expressions are not as easy to label as the basic emotion expressions parenting the mixed expressions. Therefore, for the mixed emotions of the present task we expect a smaller contribution of verbal encoding to task performance, as has been reported for face recognition memory for basic emotions Nakabayashi and Burton, We used nine different combinations of six basic emotions Plutchik, Within each block of trials, the images used for morphing mixed expressions were from a single identity. Across blocks, sex of the identities was balanced. There were four experimental blocks preceded by a practice block. The number of stimuli to be learned ranged from two targets in Block 1 to five targets in Block 4. The presentation time of the targets during the learning period changed depending on the number of targets displayed, ranging from 30 to 60 s. Across blocks, 11 targets showed morphed mixture ratios of During the learning phase, stimuli were presented simultaneously on the screen. During a delay period of approximately three minutes, participants answered a subset of questions from the Trait Meta Mood Scale Salovey et al. At retrieval, participants saw a pseudo-randomized sequence of images displaying mixed expressions. Half of the trials were learned images. The other trials differed from the learned targets in the expression mixture, in the mixture ratio, or both. There were 56 recall trials in this task. The same scoring procedures were used as in Task 7. The average performance over all trials see Table 3 was well above chance. Different scoring procedures hardly affected the rank order of individuals within the sample; the proportion correct scores were highly correlated with the d'prime scores 0. Reliability estimates suggest good psychometric quality. Further studies are needed to investigate whether learning and recognizing emotion-morphs are tapping the same ability factor as learning and recognizing prototypical expressions of emotion. Because expectations on mean differences at recognizing expression morphs are difficult to derive from a theoretical point of view, we only consider the psychometric quality of the overall score for this task. Memory span paradigms are frequently used measures of primary memory. The present task was designed as a serial cued memory task for emotion expressions of different intensity. Because recognition was required in the serial order of the stimuli displayed at learning, the sequence of presentation served as a temporal-context for memorizing facial expressions. We used FaceReader see above to score intensity levels of the stimuli chosen for this task. We used three male and four female identities throughout the task, with one identity per block. The task began with a practice block followed by seven experimental blocks of trials. Each block started with a sequence of facial expressions happiness, surprise, fear, sadness, disgust, and anger , presented one at a time, and was followed immediately by the retrieval phase. The sequence of targets at retrieval was the same as the memorized sequence. Participants were suggested to use the serial position as memory cue. Number of trials within a sequence varied between three and six. Most of the targets 25 of 33 images and distracters 37 of 54 images displayed high intensity prototypical expressions. During the learning phase stimulus presentation was fixed to ms, followed by a blank inter-stimulus interval of another ms. The position of the target in this matrix varied across trials. Distracters within a trial differed from the target in its emotional expression, intensity, or both. Participants indicated the learned expression via mouse click on the target image. Table 3 provides performance and reliability estimates. Average performance ranged between 0. Reliability estimates for the entire task are acceptable; reliability estimates for the emotion-specific trials were low; increasing the number of trials could improve reliabilities for the emotion-specific trials. We therefore recommend the overall percentage correct score as a psychometrically suitable measure of individual differences of primary memory for facial expressions. The task was to quickly detect the particular pairs and to memorize them in conjunction with their spatial arrangement on the screen. Successful detection of the pairs requires perceptual abilities. During retrieval, one expression was automatically disclosed and participants had to indicate the location of the corresponding expression. Future work might decompose perceptual and mnestic demands of this task in a regression analysis. At the beginning of a trial block several expressions initially covered with a card deck appeared as a matrix on the screen. During the learning phase, all expressions were automatically disclosed and participants were asked to detect expression pairs and to memorize their location. Then, after several seconds, the learning phase was stopped by the program, and again the cards were displayed on the screen. Next, one image was automatically disclosed and participants indicated the location of the corresponding expression with a mouse click. After the participant's response, the clicked image was revealed and feedback was given by encircling the image in green correct or red incorrect. Two seconds after the participant responded, the two images were again masked with the cards, and the next trial started by program flipping over another card to reveal a new image. Figure 4 provides a schematic representation of the trial sequence within an experimental block. Figure 4. Schematic representation of a trail block from Task 10 Memory for facial expression of emotion. Following the practice block, there were four experimental blocks of trials. Expression matrices included three one block , six one block , and nine two blocks pairs of expressions that were distributed pseudo-randomized across the lines and columns. Presentation time for learning depended on the memory set size: Within each block each image pair was used only once, resulting in 27 responses, representing the total number of trials for this task. The average proportion of correctly identified emotion pairs and reliability estimates, are summarized in Table 3. Similar to Task 9, guessing probability is much lower than 0. Reliability is also good. Due to the low number of trials within one emotion category, these reliabilities are rather poor, but could be increased by including additional trials. Pairs of happiness, surprise, anger, and fear expressions were remembered the best and sadness was remembered the worst. In the current version, we recommend the overall score as a psychometrically suitable performance indicator of memory for emotional expressions. We also developed speed indicators of emotion perception and emotion recognition ability following the same rationale as described by Herzmann et al. Tasks that are so easy that the measured accuracy levels are at ceiling allow us to gather individual differences in performance speed. Therefore, for the following tasks we used stimuli with high intensity prototypical expressions for which we expected recognition accuracy rates to be at or close to ceiling above 0. Like the accuracy tasks described above, the speed tasks were intended to measure either emotion perception three tasks or emotion recognition three tasks. Below we describe the six speed tasks and report results on their psychometric properties. Recognizing expressions from different viewpoints is a crucial socio-emotional competence relevant for everyday interaction. Here, we aimed to assess the speed of perceiving emotion expressions from different viewpoints by using a discrimination task with same-different choices. Two same-sex images with different facial identities were presented next to each other. One face was shown with a frontal view and the other in three-quarter view. Both displayed one of the six prototypical emotion expressions. Participants were asked to decide as fast and accurately as possible whether the two persons showed the same or different emotion expressions by pressing one of two marked keys on the keyboard. There was no time limit on the presentation. Participants' response started the next trial, after the presentation of a 1. Trials were pseudo-randomized in sequence and were balanced for expression match vs. To ensure high accuracy rates, confusable expressions according to the hexagon model Sprengelmeyer et al. There was a practice block of six trials with feedback. There were 31 experimental trials. Each of the six basic emotions occurred in match and mismatch trials. Average accuracies and RTs, along with average inverted latency see general description of scoring procedures above are presented in Table 4. As required for speed tasks, accuracy rates were at ceiling. RTs and inverted latencies showed that participants needed about two seconds on average to correctly match the two facial expressions presented in the frontal vs. Bonferroni-adjusted pairwise comparisons indicate the strongest difference in performance between matching emotion expressions occurred between happiness compared to all other emotions. Other statistically significant, but small effects indicated that performance matching surprise, fear, and anger expressions was faster than performance matching sadness and disgust. Reliability estimates are excellent for the overall score and acceptable for the emotion specific trials. However, happiness, surprise, and fear expressions were less frequently used in this task. Reliabilities of emotion-specific scores could be increased by using more trials in future applications. Table 4. Descriptive statistics and reliability estimates of performance speed for all speed measures of emotion perception—across all trials and for single target emotions. This task is a revision of the classic Odd-Man-Out task Frearson and Eysenck, , where several items are shown simultaneously of which one—the odd-man-out—differs from the others. Participants' task is to indicate the location of the odd-man-out. The emotion-expression version of the task—as implemented by Herzmann et al. Three faces of different identities but of the same sex , each displaying an emotion expression, were presented simultaneously in a row on the screen. The face in the center displayed the reference emotion from which either the left or right face differed in expression, whereas the remaining third face displayed the same emotion. Participants had to locate the divergent stimulus odd-man-out by pressing a key on the corresponding side. The next trial started after a 1. Again, we avoided combining highly confusable expressions of emotions in the same trial to ensure high accuracy rates Sprengelmeyer et al. Five practice trials with feedback and 30 experimental trials were administered in pseudo-randomized order. Each emotion occurred as both a target and as a distracter. Table 4 displays relevant results for this task. Throughout, accuracy rates were very high for all performance indicators, demonstrating the task to be a measure of performance speed. On average, participants needed about 2 s to detect the odd-man-out. Differences mainly occurred between happiness and all other expressions. In spite of the small number of trials per emotion category 5 , reliability estimates of the overall score based on inverted latencies are excellent and good for all emotion specific scores. We conclude that the overall task and emotion specific trial scores have good psychometric quality. The purpose of this task is to measure the speed of the visual search process see Task 3 involved in identifying an expression belonging to an indicated expression category. Here, an emotion label, a targeted emotional expression, and three mismatching alternative expressions, were presented simultaneously on the screen. The number of distracters was low in order to minimize task difficulty. Successful performance on this task requires a correct link of the emotion label and the facially expressed emotion and an accurate categorization of the expression to the appropriate semantic category. The name of one of the six basic emotions was printed in the center of the screen. The emotion label was surrounded in horizontal and vertical directions by four different face identities of the same sex all displaying different emotional expressions. Participants were asked to respond with their choice by using the arrow-keys on the number block of a regular keyboard. There were two practice trials at the beginning. Then, each of the six emotions was used eight times as a target in a pseudorandom sequence of 48 experimental trials. There were no time limits for the response, but participants were instructed to be as fast and accurate as possible. The ISI was ms. Average performance, as reflected by the three relevant scores for speed indicators, are depicted in Table 4. Accuracy rates were at ceiling. Expressions of happiness and surprise were detected the fastest, followed by disgust and anger, and finally sadness and fear. Reliability estimates were excellent for the overall score and good for emotion specific performance scores. If we are to develop computer vision and machine learning systems that can emulate this capacity, the real problem to be addressed by the community is that of precise detection of faces and facial features Ding and Martinez, Classification is less important, since this is embedded in the detection process; that is, we want to precisely detect changes that are important to recognize emotions. Most computer vision algorithms defined to date provide, however, inaccurate detections. One classical approach to detection is template matching. In this approach, we first define a template e. This template is learned from a set of sample images; for example, estimating the distribution or manifold defining the appearance pixel map of the object Yang et al. Detection of the object is based on a window search. That is, the learned template is compared to all possible windows in the image. If the template and the window are similar according to some metric, then the bounding box defining this window marks the location and size scale of the face. The major drawback of this approach is that it yields imprecise detections of the learned object, because a window of an non-centered face is more similar to the learned template than a window with background say, a tree. An example of this result is shown in Figure 7. A solution to the above problem is to learn to discriminate between non-centered windows of the objects and well centered ones Ding and Martinez, In this alternative, a non-linear classifier or some density estimator is employed to discriminate the region of the feature space defining well-centered windows of the objects and non-centered ones. This features versus context idea is illustrated in Figure 8. This approach can be used to precisely detect faces, eyes, mouth, or any other facial feature where there is a textural discrimination between it and its surroundings. Figure 9 shows some sample results of accurate detection of faces and facial features with this approach. The idea behind the features versus context approach is to learn to discriminate between the feature we wish to detect e. This approach eliminates the classical overlapping of multiple detections around the object of interest at multiple scales. At the same time, it increases the accuracy of the detection because we are moving away from poor detections and toward precise ones. Precise detections of faces and facial features using the algorithm of Ding and Martinez, The same features versus context idea can be applied to other detection and modeling algorithms, such as Active Appearance Models AAM Cootes et al. One obvious limitation is that the learned model is linear. A solution to this problem is to employ a kernel map. Kernel PCA is one option. Once we have introduced a kernel we can move one step further and use it to address additional issues of interest. A first capability we may like to add to a AAM is the possibility to work with three-dimensions. The second could be to omit the least-squares iterative nature of the Procrustes alignment required in most statistical shape analysis methods such as AAM. RIK add yet another important advantage to shape analysis: Thus, once the shape is been mapped to the RIK space, objects e. By now we know that humans are very sensitive to small changes. But we do not yet know how sensitive or accurate. Of course, it is impossible to be pixel accurate when marking the boundaries of each facial feature, because edges blur over several pixels. This can be readily observed by zooming in the corner of an eye. To estimate the accuracy of human subjects, we performed the following experiment. First, we designed a system that allows users to zoom in at any specified location to facilitate delineation of each of the facial features manually. Second, we asked three people herein referred to as judges to manually delineate each of the facial components of close to 4, images of faces. Third, we compared the markings of each of the three judges. The within-judge variability was on average 3. This gives us an estimate of the accuracy of the manual detections. The average error of the algorithm of Ding and Martinez is 7. Thus, further research is needed to develop computer vision algorithms that can extract even more accurate detection of faces and its components. Another problem is what happens when the resolution of the image diminishes. Humans are quite robust to these image manipulations Du and Martinez, One solution to this problem is to use manifold learning. In particular, we wish to define a non-linear mapping f. This is illustrated in Figure That is, given enough sample images and their shape feature vectors described in the preceding section, we need to find the function which relates the two. This can be done, for example, using kernel regression methods Rivera and Martinez, One of the advantages of this approach is that this function can be defined to detect shape from very low resolution images or even under occlusions. Example detections using this approach are shown in Figure Manifold learning is ideal for learning mappings between face object images and their shape description vectors. Shape detection examples at different resolutions. Note how the shape estimation is almost as good regardless of the resolution of the image. Recent advances in non-rigid structure from motion allow us to recover very accurate reconstructions of both the shape and the motion even under occlusion. A recent approach resolves the nonlinearity of the problem using kernel mappings Gotardo and Martinez, b. Combining the two approaches to detection defined in this section should yield even more accurate results in low-resolution images and under occlusions or other image manipulations. We hope that more research will be devoted to this important topic in face recognition. The approaches defined in this section are a good start, but much research is needed to make these systems comparable to human accuracies. We argue that research in machine learning should address these problems rather than the typical classification one. A first goal is to define algorithms that can detect face landmarks very accurately even at low resolutions. Kernel methods and regression approaches are surely good solutions as illustrated above. But more targeted approaches are needed to define truly successful computational models of the perception of facial expressions of emotion. In the real world, occlusions and unavoidable imprecise detections of the fiducial points, among others, are known to affect recognition Torre and Cohn, ; Martinez, Additionally, some expressions are, by definition, ambiguous. Most importantly though seems to be the fact that people are not very good at recognizing facial expressions of emotion even under favorable condition Du and Martinez, Humans are very robust at detection joy and surprise from images of faces; regardless of the image conditions or resolution. However, we are not as good at recognizing anger and sadness and are worst at fear and disgust. The above results suggest that there could be three groups of expressions of emotion. The first group is intended for conveying emotions to observers. These expressions have evolved a facial construct i. Example expressions in this group are happiness and surprise. A computer vision system—especially a HCI—should make sure these expressions are accurately and robustly recognized across image degradation. Therefore, we believe that work needs to be dedicated to make systems very robust when recognizing these emotions. The second group of expressions e. A computer vision system should recognize these expressions in good quality images, but can be expected to fail as the image degrades due to resolution or other image manipulations. An interesting open question is to determine why this is the case and what can be learned about human cognition from such a result. The third and final group of emotions constitutes those at which humans are not very good recognizers. This includes expressions such as fear and disgust. Early work especially in evolutionary psychology had assumed that recognition of fear was primal because it served as a necessary survival mechanism LeDoux, Recent studies have demonstrated much the contrary. Fear is generally poorly recognized by healthy human subjects Smith and Schyns, ; Du and Martinez, One hypothesis is that expressions in this group have evolved for other than communication reasons. For example, it has been proposed that fear opens sensory channels i. Note that people can be trained to detect such changes quite reliably Ekman and Rosenberg, , but this is not the case for the general population. Another area that will require additional research is to exploit other types of facial expressions. Facial expressions are regularly used by people in a variety of setting. More research is needed to understand these. Moreover, it will be important to test the model in natural occurring environments. Collection and handling of this data poses several challenges, but the research described in these pages serves as a good starting point for such studies. In such cases, it may be necessary to go beyond a linear combination of basic categories. However, without empirical proof for the need of something more complex than linear combinations of basic emotion categories, such extensions are unlikely. The cognitive system has generally evolved the simplest possible algorithms for the analysis or processing of data. Strong evidence of more complex models would need to be collected to justify such extensions. One way to do this is by finding examples that cannot be parsed by the current model, suggesting a more complex structure is needed. It is important to note that these results will have many applications in studies of agnosias and disorders. Of particular interest are studies of depression or anxiety disorders. Depression afflicts a large number of people in the developed countries. Models that can help us better understand its cognitive processes, behaviors and patterns could be of great importance for the design of coping mechanisms. Improvements may also be possible if it were to better understand how facial expressions of emotion affect these people. Other syndromes such as autism are also of great importance these days. More children than ever are being diagnosed with the disorder CDC, ; Prior, We know that autistic children do not perceive facial expressions of emotion as others do Jemel et al. A modified computational model of the perception of facial expressions of emotion in autism could help design better teaching tools for this group and may bring us closer to understanding the syndrome. There are indeed many great possibilities for machine learning researchers to help move these studies forward. Extending or modifying the modeled summarized in the present paper is one way. Developing machine learning algorithms to detect face landmark more accurately is another. Developing statistical tools that more accurately represent the underlying manifold or distribution of the data is yet another great way to move the state of the art forward. In the present work we have summarized the development of a model of the perception of facial expressions of emotion by humans. A key idea in this model is to linearly combine a set of face spaces defining some basic emotion categories. The model is consistent with our current understanding of human perception and can be successfully exploited to achieve great recognition results for computer vision and HCI applications. We have shown how, to be consistent with the literature, the dimensions of these computational spaces need to encode configural and shape features. We conclude that to move the state of the art forward, face recognition research has to focus on a topic that has received little attention in recent years—precise, detailed detection of faces and facial features. Although we have focused our study on the recognition of facial expressions of emotion, we believe that the results apply to most face recognition tasks. We have listed a variety of ways in which the machine learning community can get involved in this research project and briefly discussed applications in the study of human perception and the better understanding of disorders. J Mach Learn Res. Author manuscript; available in PMC Aug Many phrases in sign language include facial expressions in the display. There is controversy surrounding the question of whether facial expressions are worldwide and universal displays among humans. Supporters of the Universality Hypothesis claim that many facial expressions are innate and have roots in evolutionary ancestors. Opponents of this view question the accuracy of the studies used to test this claim and instead believe that facial expressions are conditioned and that people view and understand facial expressions in large part from the social situations around them. Moreover, facial expressions have a strong connection with personal psychology. Some psychologists have the ability to discern hidden meaning from person's facial expression. One experiment investigated the influence of gaze direction and facial expression on face memory. Participants were shown a set of unfamiliar faces with either happy or angry facial expressions, which were either gazing straight ahead or had their gaze averted to one side. Memory for faces that were initially shown with angry expressions was found to be poorer when these faces had averted as opposed to direct gaze, whereas memory for individuals shown with happy faces was unaffected by gaze direction. It is suggested that memory for another individual's face partly depends on an evaluation of the behavioural intention of that individual. Facial expressions are vital to social communication between humans. They are caused by the movement of muscles that connect to the skin and fascia in the face. These muscles move the skin, creating lines and folds and causing the movement of facial features, such as the mouth and eyebrows. These muscles develop from the second pharyngeal arch in the embryo. The temporalis , masseter , and internal and external pterygoid muscles , which are mainly used for chewing, have a minor effect on expression as well. These muscles develop from the first pharyngeal arch. There are two brain pathways associated with facial expression; the first is voluntary expression. Voluntary expression travels from the primary motor cortex through the pyramidal tract , specifically the corticobulbar projections. The cortex is associated with display rules in emotion, which are social precepts that influence and modify expressions. Cortically related expressions are made consciously. The second type of expression is emotional. These expressions originate from the extrapyramidal motor system , which involves subcortical nuclei. For this reason, genuine emotions are not associated with the cortex and are often displayed unconsciously. This is demonstrated in infants before the age of two; they display distress, disgust, interest, anger, contempt, surprise, and fear. Infants' displays of these emotions indicate that they are not cortically related. Similarly, blind children also display emotions, proving that they are subconscious rather than learned. Other subcortical facial expressions include the "knit brow" during concentration, raised eyebrows when listening attentively, and short "punctuation" expressions to add emphasis during speech. People can be unaware that they are producing these expressions. The amygdala plays an important role in facial recognition. Functional imaging studies have found that when shown pictures of faces, there is a large increase in the activity of the amygdala. It is believed that the emotion disgust is recognized through activation of the insula and basal ganglia. The recognition of emotion may also utilize the occipitotemporal neocortex, orbitofrontal cortex and right frontoparietal cortices. More than anything though, what shapes a child's cognitive ability to detect facial expression is being exposed to it from the time of birth. The more an infant is exposed to different faces and expressions, the more able they are to recognize these emotions and then mimic them for themselves. Infants are exposed to an array of emotional expressions from birth, and evidence indicates that they imitate some facial expressions and gestures e. A person's face, especially their eyes, creates the most obvious and immediate cues that lead to the formation of impressions. A person's eyes reveal much about how they are feeling, or what they are thinking. Blink rate can reveal how nervous or at ease a person may be. Research by Boston College professor Joe Tecce suggests that stress levels are revealed by blink rates. He supports his data with statistics on the relation between the blink rates of presidential candidates and their success in their races. Tecce claims that the faster blinker in the presidential debates has lost every election since .

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Please review our privacy policy. Sadly surprised. Compound emotion [ 17 ]. Seven emotions and 22 compound emotions. Motion facilitates emotion recognition from faces Category emotion expression face facial. Kamachi et al. Ambadar et al.

Ddlg videos Watch PORN Videos Xxx Maestros. Each classifier is specifically designed to recognize a single emotion label, such as surprise. Several psychophysical experiments suggest the perception of emotions by humans is categorical Ekman and Rosenberg, Studies in neuroscience further suggest that distinct regions or pathways in the brain are used to recognize different expressions of emotion Calder et al. An alternative to the categorical model is the continuous model Russell, ; Rolls, Here, each emotion is represented as a feature vector in a multidimensional space given by some characteristics common to all emotions. This model can justify the perception of many expressions, whereas the categorical model needs to define a class i. It also allows for intensity in the perception of the emotion label. Whereas the categorical model would need to add an additional computation to achieve this goal Martinez, , in the continuous model the intensity is intrinsically defined in its representation. Yet, morphs between expressions of emotions are generally classified to the closest class rather than to an intermediate category Beale and Keil, Perhaps more interestingly, the continuous model better explains the caricature effect Rhodes et al. This is because the farther the feature vector representing that expression is from the mean or center of the face space , the easier it is to recognize it Valentine, In neuroscience, the multidimensional or continuous view of emotions was best exploited under the limbic hypothesis Calder et al. Under this model, there should be a neural mechanism responsible for the recognition of all facial expressions of emotion, which was assumed to take place in the limbic system. Recent results have however uncovered dissociated networks for the recognition of most emotions. This is not necessarily proof of a categorical model, but it strongly suggests that there are at least distinct groups of emotions, each following distinct interpretations. Furthermore, humans are only very good at recognizing a number of facial expressions of emotion. The most readily recognized emotions are happiness and surprise. It has been shown that joy and surprise can be robustly identified extremely accurately at almost any resolution Du and Martinez, Figure 1 shows a happy expression at four different resolutions. The reader should not have any problem recognizing the emotion in display even at the lowest of resolutions. However, humans are not as good at recognizing anger and sadness and are even worse at fear and disgust. Happy faces at four different resolutions. From left o right: All images have been resized to a common image size for visualization. A major question of interest is the following. Why are some facial configurations more easily recognizable than others? One possibility is that expressions such as joy and surprise involve larger face transformations than the others. This has recently proven not to be the case Du and Martinez, While surprise does have the largest deformation, this is followed by disgust and fear which are poorly recognized. Learning why some expressions are so readily classified by our visual system should facilitate the definition of the form and dimensions of the computational model of facial expressions of emotion. The search is on to resolve these two problems. First, we need to determine the form of the computational space e. Second, we ought to define the dimensions of this model e. In the following sections we overview the research we have conducted in the last several years leading to a solution to the above questions. We then discuss on the implications of this model. In particular, we provide a perspective on how machine learning and computer vision researcher should move forward if they are to define models based on the perception of facial expressions of emotion by humans. In cognitive science and neuroscience researchers have been mostly concerned with models of the perception and classification of the six facial expressions of emotion listed above. Similarly, computer vision and machine learning algorithms generally employ a face space to represent these six emotions. Sample feature vectors or regions of this feature space are used to represent each of these six emotion labels. This approach has a major drawback—it can only detect one emotion from a single image. In machine learning, this is generally done by a winner-takes-all approach Torre and Cohn, This means that when a new category wants to be included, one generally needs to provide labeled samples of it to the learning algorithm. Yet, everyday experience demonstrates that we can perceive more than one emotional category in a single image Martinez, , even if we have no prior experience with it. For example, Figure 2 shows images of faces expressing different surprises—happily surprised, angrily surprised, fearfully surprised, disgustedly surprised and the typically studied surprise. Faces expressing different surprise. From left to right: If we were to use a continuous model, we would need to have a very large number of labels represented all over the space; including all possible types of surprises. This would require a very large training set, since each possible combination of labels would have to be learned. But this is the same problem a categorical model would face. In such a case, dozens if not hundreds of sample images for each possible category would be needed. Alternatively, Susskind et al. If we define an independent computational face space for a small number of emotion labels, we will only need sample faces of those few facial expressions of emotion. This is indeed the approach we have taken. Details of this model are given next. Key to this model is to note that we can define new categories as linear combinations of a small set of categories. Figure 3 illustrates this approach. In this figure, we show how we can obtain the above listed different surprises as a linear combination of known categories. A large number of such expressions exist that are a combination of the six emotion categories listed above and, hence, the above list of six categories is a potential set of basic emotion classes. Also, there is some evidence form cognitive science to suggest that these are important categories for humans Izard, Of course, one needs not base the model on this set of six emotions. This is an area that will undoubtedly attract lots of interest. A question of particular interest is to determine not only which basic categories to include in the model but how many. To this end both, cognitive studies with humans and computational extensions of the proposed model will be necessary, with the results of one area aiding the research of the other. This figure shows how to construct linear combinations of known categories. At the top of the figure, we have the known or learned categories emotions. The coefficients s i determine the contribution of each of these categories to the final perception of the emotion. The approach described in the preceding paragraph would correspond to a categorical model. However, we now go one step further and define each of these face spaces as continuous feature spaces, Figure 3. This allows for the perception of each emotion at different intensities, for example, less happy to exhilarant Neth and Martinez, Less happy would correspond to a feature vector in the left most face space in the figure closer to the mean or origin of the feature space. Feature vectors farther from the mean would be perceived as happier. The proposed model also explains the caricature effect, because within each category the face space is continuous and exaggerating the expression will move the feature vector representing the expression further from the mean of that category. In essence, the intensity observed in this continuous representation defines the weight of the contribution of each basic category toward the final decision classification. It also allows for the representation and recognition of a very large number of emotion categories without the need to have a categorical space for each or having to use many samples of each expression as in the continuous model. The proposed model thus bridges the gap between the categorical and continuous ones and resolves most of the debate facing each of the models individually. To complete the definition of the model, we need to specify what defines each of the dimensions of the continuous spaces representing each category. We turn to this problem in the next section. In the early years of computer vision, researchers derived several feature- and shape-based algorithms for the recognition of objects and faces Kanade, ; Marr, ; Lowe, In these methods, geometric, shape features and edges were extracted from an image and used to build a model of the face. This model was then fitted to the image. Good fits determined the class and position of the face. Later, the so-called appearance-based approach, where faces are represented by their pixel-intensity maps or the response of some filters e. In this alternative texture-based approach, a metric is defined to detect and recognize faces in test images Turk and Pentland, Advances in pattern recognition and machine learning have made this the preferred approach in the last two decades Brunelli and Poggio, Inspired by this success, many algorithms developed in computer vision for the recognition of expressions of emotion have also used the appearance-based model Torre and Cohn, The appearance-based approach has also gained momentum in the analysis of AUs from images of faces. The main advantage of the appearance-based model is that one does not need to predefine a feature or shape model as in the earlier approaches. Rather, the face model is inherently given by the training images. The appearance-based approach does provide good results from near-frontal images of a reasonable quality, but it suffers from several major inherent problems. The main drawback is its sensitivity to image manipulation. Image size scale , illumination changes and pose are all examples of this. Most of these problems are intrinsic to the definition of the approach since this cannot generalize well to conditions not included in the training set. One solution would be to enlarge the number of training images Martinez, However, learning from very large data sets in the order of millions of samples is, for the most part, unsolved Lawrence, Progress has been made in learning complex, non-linear decision boundaries, but most algorithms are unable to accommodate large amounts of data—either in space memory or time computation. This begs the question as to how the human visual system solves the problem. One could argue that, throughout evolution, the homo genus and potentially before it has been exposed to trillions of faces. This has facilitated the development of simple, yet robust algorithms. In computer vision and machine learning, we wish to define algorithms that take a shorter time to learn a similarly useful image representation. One option is to decipher the algorithm used by our visual system. Research in face recognition of identity suggests that the algorithm used by the human brain is not appearance-based Wilbraham et al. Rather, it seems that, over time, the algorithm has identified a set of robust features that facilitate rapid categorization Young et al. This is also the case in the recognition of facial expressions of emotion Neth and Martinez, Figure 4 shows four examples. These images all bear a neutral expression, that is, an expression associated to no emotion category. Yet, human subjects perceive them as expressing sadness, anger, surprise and disgust. The most striking part of this illusion is that these faces do not and cannot express any emotion, since all relevant AUs are inactive. This effect is called over-generalization Zebrowitz et al. The four face images and schematics shown above all correspond to neutral expressions i. Yet, most human subjects interpret these faces as conveying anger, sadness, surprise and disgust. Note that although these faces look very different from one another, three of them are actually morphs from the same original image. The images in Figure 4 do have something in common though—they all include a configural transformation. What the human visual system has learned is that faces do not usually look like those in the image. Rather the relationship distances between brows, nose, mouth and the contour of the face is quite standard. They follow a Gaussian distribution with small variance Neth and Martinez, The images shown in this figure however bear uncanny distributions of the face components. In the sad-looking example, the distance between the brows and mouth is larger than normal Neth and Martinez, and the face is thinner than usual Neth and Martinez, During post-processing of the images, differences in skin texture were adjusted and non-facial cues, like ears, hair and clothing, were eliminated. Physical attributes like luminance and contrast were held constant across images. Each task was balanced with an equal number of female and male stimuli. Whenever two different identities were simultaneously presented in a given trial, portraits of same sex models were used. All tasks were administered by trained proctors in group-sessions with up to 10 participants. Sessions were completed in approximately weekly intervals. Both task and trial sequences were kept constant across all participants. Computers with inch monitors screen definition: The tasks were programmed in Inquisit 3. Each task started at the same time for all participants in a given group. In general, participants were asked to work to the best of their ability as quickly as possible. They were instructed to use the left and right index fingers during tasks that used two response options and to keep the fingers positioned directly above the relevant keys throughout the whole task. Tasks with four response options were organized such that the participant only used the index finger of a preferred hand. Every single task was introduced by proctors and additional instructions were provide on screen. There were short practice blocks in each task consisting of at least 5 and at most 10 trials depending on task difficulty with trial-by-trial feedback about accuracy. There was no feedback for any of the test trials. Table 1 gives an overview of the tasks included in the task battery. Outliers in univariate distributions were set to missing. For the approximately 0. With this procedure, plausible values were computed as predicted values for missing observations plus a random draw from the residual normal distribution of the respective variable. One of the multiple datasets was used for the analyses reported here. Results were verified and do not differ from datasets obtained through multiple imputation with the R package mice, by van Buuren and Groothuis-Oudshoorn Reaction time RT scores were only computed from correct responses. RTs smaller than ms were set to missing, because they were considered too short to represent proper processing. The remaining RTs were winsorized e. This procedure was repeated iteratively beginning with the slowest response until there were no more RTs above the criterion of 3 SD. All analyses were conducted with the statistical software environment R. For accuracy tasks, we defined the proportion of correctly solved trials of an experimental condition of interest e. For some of these tasks we applied additional scoring procedures as indicated in the corresponding task description. Speed indicators were average inverted RTs measures in seconds obtained across all correct responses associated with the trials from the experimental conditions of interest. Note that accuracy was expected to be at ceiling in measures of speed. Inverted latency was calculated as divided by the RT in milliseconds. Calder et al. Composite facial expressions were created by aligning the upper and the lower face half of the same person, but from photos with different emotional expressions, so that in the final photo each face was expressing an emotion in the upper half of the face that differed from the emotion expressed in the lower half of the face. Aligned face halves of incongruent expressions lead to holistic interference. It has been shown that an emotion expressed in only one face half is less accurately recognized compared to congruent emotional expressions in face composites e. In order to avoid ceiling effects, as is common for the perception of emotions from prototypical expressions, we took advantage of the higher task difficulty imposed by combining different facial expressions in the top and bottom halves of faces, and exploited the differential importance of the top and bottom face for the recognition of specific emotions Ekman et al. Specifically, fear, sadness, and anger are more readily recognized in the top half of the face and happiness, surprise, and disgust in the bottom half of the face Calder et al. Here, we used the more readily recognizable halves for the target halves in order to ensure acceptable performance. Top halves expressing fear, sadness, or anger were only combined with bottom halves expressing disgust, happiness, or surprise—yielding nine different composites see Figure 1 for examples of all possible composite expression stimuli of a female model. Figure 1. Stimuli examples used in Task 1 Identification of emotion expression from composite faces. After the instruction and nine practice trials, 72 experimental trials were administered. The trial sequence was random across the nine different emotion composites. Pictures with emotional expressions of four female and four male models were used to create the 72 emotion composites. For each model, nine aligned composite faces were created. In each trial, following a fixation cross, a composite face was presented in the center of the screen. Participants were asked to click with a computer mouse the button corresponding to the emotion in the prompted face half. After the button was clicked the face disappeared and the screen remained blank for ms; then the next trial started with the fixation cross. In addition to the proportion of correct responses across a series of 72 trials, we calculated unbiased hit rates H u ; Wagner, Unbiased hit rates account for response biases toward a specific category and correct for systematic confusions between emotion categories. For a specific emotion score H u was calculated as squared frequency of the correct classifications divided by the product of the number of stimuli used for the different emotion categories and the overall frequency of choices for the target emotion category. We report difficulty estimates for both percent correct and H u. We calculated reliabilities on the basis of percent correct scores. Difficulty estimates in Table 2 based on percent correct scores show that performance was not at ceiling. Post-hoc analyses indicate happiness was recognized the best, followed by surprise, anger, disgust, fear, and sadness. This ranking was similar for H u scores see Table 2. However, when response biases were controlled for, anger was recognized better than surprise. Percent correct and H u scores across all trials were correlated 0. Table 2. Descriptive statistics and reliability estimates of performance accuracy for all emotion perception tasks across all trials and for single target emotions. Reliability estimates across all trials were very good and across all trials for a single emotion, considering the low number of trials for single emotions and the unavoidable heterogeneity of facial stimuli, were satisfactory ranging between 0. Difficulty estimates suggest that performance across persons was not at ceiling. The psychometric quality of single emotion expression scores and performance on the overall measure are satisfactory to high. Adding more trials to the task could further increase the reliability of the emotion specific performance indicators. Motion facilitates emotion recognition from faces e. Kamachi et al. Ambadar et al. In Task 2, we used dynamic stimuli in order to extend the measurement of emotion identification to more real life-like situations and to ensure adequate construct representation of the final task battery Embretson, Because previous findings predict higher accuracy rates for emotion identification from dynamic stimuli, we implemented intensity manipulations in order to avoid ceiling effects. Hess et al. We generated expression-end-states by morphing intermediate expressions between a neutral and an emotional face. Mixture ratios for the morphs aimed at three intensity levels by decreasing the proportion of neutral relative to the full emotion expressions from In order to capture the contrast between configural vs. Face inversion strongly impedes holistic processing, allowing mainly feature-based processing Calder et al. McKelvie indicated an increase of errors and RTs of emotion perception from static faces presented upside-down and similar findings were reported for dynamic stimuli as well Ambadar et al. The first frame of the video displayed a neutral facial expression that, across the subsequent frames, changed to an emotional facial expression. The videos ended at ms and the peak expression displayed in the last frame remained on the screen until the categorization was performed. Emotion label buttons were the same as in the previous task. We varied expression intensity across trials, with one third of the trials for each intensity level. The morphing procedure was similar to the procedure used in previous studies e. First, static pictures were generated by morphing a neutral expression image of a face model with the images of the same person showing one of the 6 basic emotions; mixture ratios were 40, 60, or 80 percent of the emotional face. Second, short video sequences were produced on the basis of a morphed sequence of frames starting from a neutral expression and ending with one of emotional faces generated in the first step. Thus, video sequences were created for all three intensities; this was done separately for two female and two male models. Half of the 72 trials were presented upright and the other presented upside down. Following the instructions participants completed four practice trials. The experimental trials with varying conditions upright vs. In addition to results for the percent correct scores, we also report unbiased hit rates see above. Table 2 summarizes the average performance calculated for both, percent correct and unbiased hit rates the scores are correlated 0. It seems that the facial expressions of anger used here were particularly heterogeneous. There were no ceiling effects in any of the indicators. An rmANOVA with factors for emotion expression and expression intensity revealed main effects for both. The rank order of recognizability of different emotional expressions was comparable with Task 1, which used expression composites cf. Figures 2A,B. Happiness and surprise were recognized the best, followed by anger and disgust, and finally sadness and fear were the most difficult. Scores calculated across all trials within single emotions disregarding the intensity manipulation had acceptable or good psychometric quality. Figure 2. Plots of the rank order of recognizability of the different emotion categories esteemed in emotion perception task. A Task 1, Identification of Emotion Expression from composite faces; B Task 2, Identification of Emotion Expression of different intensity from upright and inverted dynamic face stimuli; C Task 3, Visual search for faces with corresponding Emotion Expression of different intensity, error bars represent confidence intervals. Task 3 was inspired by the visual search paradigm often implemented for investigating attention biases to emotional faces e. In general, visual search tasks require the identification of a target object that differs in at least one feature e. In this task, participants had to recognize several target facial expressions that differed from a prevailing emotion expression. Usually, reaction time slopes are inspected as dependent performance variables in visual search tasks. However, we set no limits on response time and encouraged participants to screen and correct their responses before confirming their choice. This way we aimed to minimize the influence of visual saliency of different emotions on the search efficiency due to pre-attentive processes Calvo and Nummenmaa, and capture intentional processing instead. This task assessed the ability to discriminate between different emotional facial expressions. The majority of the images displayed one emotional expression surprise, fear, sadness, disgust, or anger referred to here as the target expression. In each trial participants were asked to identify the neutral and emotional expressions. Experimental manipulations incorporated in each trial were: Happiness expressions were not used in this task because performance for smiling faces was assumed to be at ceiling due to pop out effects. The location of target stimuli within the grid was pseudo-randomized. Participants' task was to identify and indicate all distracter expressions by clicking with their mouse a tick box below each stimulus. The task aimed to implement two levels of difficulty by using target and distracter expressions with low and high intensity. Figure 3. Schematic representation of a trail from Task 3 Visual search for faces with corresponding emotion expression of different intensity. All stimuli were different images originating from four models two females and two males. Intensity level was assessed with the FaceReader software. Based on these intensity levels, trials were composed of either low or high intense emotion stimuli for targets as well as for distracters within the same trial. The number of divergent expressions to be identified was distributed uniformly across conditions. There were 40 experimental trials administered after three practice trials, which followed the instructions. The accuracies of the multiple answers for a trial are dependent variables. We applied three different scoring procedures. The first was based on the proportion of correctly recognized targets. This procedure only accounts for the hit rates, disregards false alarms, and can be used to evaluate the detection rate of target facial expressions. For the second, we computed a difference score between the hit-rate and false-alarm rate for each trial. This score is an indicator of the ability to recognize distracter expressions. Next, we will report proportion correct scores. Table 2 additionally displays average performance based on the d'prime scores. The univariate distributions of emotion-specific performance indicators and the average performance—displayed in Table 2 —suggest substantial individual differences in accuracy measures. The task design was successful at avoiding ceiling effects frequently observed for recognition performance of prototypical expressions. This was presumably achieved by using stimuli of varying expression intensity and by the increasing number of distracters across trials. Considering that only eight trials entered the emotion specific scores and that emotional expressions are rather heterogeneous, reliability estimates ranging from 0. The rank orders of recognizability of the emotion categories were slightly different from those estimated in Task 1 and 2 see Figures 2C,B compared with Figures 2A,B. Surprised faces were recognized the best, as was the case for Task 2. Anger faces were recognized considerably worse than sadness faces. This inconsistency might be due to effects of stimulus sampling. Performance on fear expressions was the poorest. The difficulty manipulation based on high vs. The ratios of the difference between low and high intensity conditions varied across emotions: We conclude that performance indicators derived from this task have acceptable psychometric quality. Empirical difficulty levels differ across the intended manipulations based on expression intensity and the task revealed a rank order of recognizability similar to other tasks used in this study. The scoring procedure hardly affected the rank order of persons, allowing the conclusion that different scores derived from this task express the same emotional expression discrimination ability. It is suggested that the encoding of facial emotion expressions is based on discrete categorical qualitative matching Etcoff and Magee, ; Calder et al. There is evidence that both types of perception are integrated and used complementary Fujimura et al. In this task, we required participants to determine the mixture ratios of two prototypical expressions of emotions. In order to avoid memory-related processes we constructed a simultaneous matching task. We morphed expressions of two emotions along a continuum of 10 mixture ratios. We only morphed continua between adjacent emotions on a so-called emotion hexagon with the sequence happiness-surprise-fear-sadness-disgust-anger , where proximity of emotions represents potentially stronger confusion between expressions e. In terms of categorical perception, there should be an advantage in identifying the correct mixture-ratio at the end of a continuum compared with more balanced stimuli in the middle of the continuum between two expression categories Calder et al. Morphed images were created from two different expressions with theoretically postulated and empirically tested maximal confusion rates Ekman and Friesen, Thus, morphs were created on the following six continua: These morphs were created for each face separately for five female and five male models. In every trial, two images of the same identity were presented on the upper left and on the upper right side of the screen, where each image displayed a different prototypical emotion expression happiness, surprise, fear, sadness, disgust, and anger. Below these faces, centered on the screen, was a single expression morphed from the prototypical faces displayed in the upper part of the screen. All three faces remained on the screen until participants responded. Participants were asked to estimate the ratio of the morphed photo on a continuous visual analog scale. Participants were asked to estimate the mixture-ratio of the morph photo as exactly as possible, using the full range of the scale. There were no time limits. Three practice trials preceded 60 experimental trials. We scored performance accuracy as the average absolute deviation of participants' response from the correct proportion of the mixture between the two parent expressions. Table 2 displays the average overall and emotion specific deviation scores. Further, it was interesting to investigate whether performance was higher toward the ends of the continua as predicted by categorical accounts of emotional expression perception. A series of two-tailed paired t -tests compared differences between the emotion categories of the parent photo. The correct mixture ratio was better identified in the following combinations: Generally, we expected mixtures of more similar expressions to bias the evaluation of the morphs. The results are essentially in line with these expectations based on expression similarities. Taken together, the results suggest the deviation scores meet psychometric standards. Performance improved or worsened as predicted by theories of categorical perception. Future research should examine whether expression assignment in morphed emotions is indicative of the ability to identify prototypical emotion expressions. This task is a forced choice version of the previously described Task 4 and aims to measure categorical perception of emotional expressions using a further assessment method. Participants were asked to decide whether the morphed expression presented in the upper middle of the screen was more similar to the expression prototype displayed on the lower left or lower right side of the screen. Stimuli were identical with those used in Task 4, but the sequence of presentation was different. The task design differed from that of Task 4 only in that participants were forced to decide whether the expression-mix stimulus was composed of more of the left or more of the right prototypical expression. Response keys were the left and right control keys on the regular computer keyboard, which were marked with colored tape. The average percentages of correct decisions are given in Table 2. This task was rather easy compared with Tasks 1—3. The distribution of the scores was, however, not strongly skewed to the right, but rather followed a normal distribution with most of the participants performing within the range of 0. Similarly to Task 4, the rank order of emotion recognizability was not similar to Tasks 1 or 2. Generally, the psychometric properties of this task need improvement and further studies should address the question whether forced-choice expression assignment in emotion-morphs is indicating the same ability factor indicated by the other tasks i. The following five tasks arguably assess individual differences in memory-related abilities in the domain of facial expressions. All tasks consist of a learning phase for facial expressions and a subsequent retrieval phase that requires recognition or recall of previously learned expressions. The first three memory tasks include an intermediate task between learning and recall of at least three minutes, hence challenging long-term retention. In Task 9 and 10, learning is immediately followed by retrieval. With this forced-choice SM task we aimed to assess the ability to learn and recognize facial expressions of different intensity. Emotion category, emotion intensity, and learning-set size varied across trials, but face identity was constant within a block of expressions that the participant was asked to learn together. Manipulations of expression intensity within targets, but also between targets and distracters, were used to increase task difficulty. The recognition of expression intensity is also a challenge in everyday life; hence, the expression intensity manipulation is not restricted to psychometric rationales. We expected hit-rates to decline with increasing ambiguity for less intense targets e. We administered one practice block of trials and four experimental blocks—including four face identities half were females and 18 trials per block. Each block started by presenting a set of target faces of the same face identity but with different emotion expressions. To-be-learned stimuli were presented simultaneously in a line centered on the screen. Experimental blocks differed in the number of targets, expressed emotion, expression intensity, and presentation time. Presentation time ranged from 30 to 60 s depending on the number of targets within a block two up to five stimuli. Facial expressions of six emotions were used as targets as well as distracters happiness, surprise, anger, fear, sadness, and disgust. Participants were instructed to remember the combination of both expression and intensity. During a delay phase of about three minutes, participants worked on a two-choice RT task they had to decide whether two simultaneous presented number series are the same or different. Recall was structured as a pseudo-randomized sequence of 18 single images of targets or distracters. Targets were identical with the previously learned expressions in terms of emotional content and intensity, but different photographs of the same identities were used in order to reduce effects of simple image recognition. Distracters differed from the targets in both expression content and intensity. Participants were requested to provide a two-choice discrimination decision between learned and distracter expressions on the keyboard. After a response, the next stimulus was presented. The average performance accuracy over all trials and across trials of specific emotion categories is presented in Table 3. Pairwise comparison based on adjusted p -values for simultaneous inference using the Bonferroni-method showed that participants were better at recognizing happiness relative to all other emotions. Additionally, expressions of anger were significantly better retrieved than surprise, fear, or disgust expressions. There were no additional performance differences due to emotion content. Table 3. Descriptive statistics and reliability estimates of performance accuracy for all emotion memory tasks—across all trials and for single target emotions if applicable. Intensity manipulation was partly successful: While performance on low intensity stimuli was slightly better than performance on medium intensity stimuli, we believe this effect reflects a Type 1 error and will not replicate in an independent sample. We recommend using high vs. Reliability estimates are provided in Table 3 and suggest good psychometric properties for the overall task. Reliabilities for emotion-specific trials are acceptable considering the low number of indicators and the heterogeneity of facial emotion expressions in general. In sum, we recommend using two levels of difficulty and the overall performance as indicators of expression recognition accuracy for this task. In this delayed recognition memory task, we displayed facial expressions with a frontal view as well as right and left three-quarter views. We aimed to assess long-term memory bindings between emotion expressions and face orientation. Infants prefer to look at faces that engage them in mutual gaze and that, from an early age, healthy babies show enhanced neural processing of direct gaze. Eye contact is another major aspect of facial communication. Some have hypothesized that this is due to infancy, as humans are one of the few mammals who maintain regular eye contact with their mother while nursing. It regulates conversations, shows interest or involvement, and establishes a connection with others. But different cultures have different rules for eye contact. Certain Asian cultures can perceive direct eye contact as a way to signal competitiveness, which in many situations may prove to be inappropriate. Others lower their eyes to signal respect, and similarly eye contact is avoided in Nigeria; [16] however, in western cultures this could be misinterpreted as lacking self-confidence. Even beyond the idea of eye contact, eyes communicate more data than a person even consciously expresses. Pupil dilation is a significant cue to a level of excitement, pleasure, or attraction. Dilated pupils indicate greater affection or attraction, while constricted pupils send a colder signal. Facial expression is used in sign languages to convey specific meanings. Lowered eyebrows are used for wh-word questions. Facial expression is also used in sign languages to show adverbs and adjectives such as distance or size: It can also show the manner in which something is done, such as carelessly or routinely. The belief in the evolutionary basis of these kinds of facial expressions can be traced back to Darwin 's The Expression of the Emotions in Man and Animals. Reviews of the universality hypothesis have been both supportive [19] [20] and critical. Ekman 's work on facial expressions had its starting point in the work of psychologist Silvan Tomkins. Ekman showed that facial expressions of emotion are not culturally determined, but universal across human cultures. To demonstrate his universality hypothesis, Ekman ran a test on a group of the South Fore people of New Guinea , a pre-industrial culture that was isolated from the West. The experiment participants were told brief stories about emotional events happiness, sadness, anger, fear, surprise, and disgust. After each story, they were asked to select the matching facial expression from an array of three faces. Children selected from an array of only two faces, and their results were similar to the adults'. Subsequent cross-cultural studies found similar results. Both sides of this debate agree that the face expresses emotion. One argument against the evidence presented in support of the universality hypothesis is that the method typically used to demonstrate universality inflates recognition scores. The three main factors are the following:. Darwin argued that the expression of emotions has evolved in humans from animal ancestors, who would have used similar methods of expression. Darwin believed that expressions were unlearned and innate in human nature and were therefore evolutionarily significant for survival. He compiled supporting evidence from his research on different cultures, on infants, and in other animal species. Cross-cultural studies had shown that there are similarities in the way emotions are expressed across diverse cultures, but studies have even shown that there are similarities between species in how emotions are expressed. Research has shown that chimpanzees are able to communicate many of the same facial expressions as humans through the complex movements of the facial muscles. In fact, the facial cues were so similar that Ekman's Facial Action Coding System could be applied to the chimps in evaluating their expressions. Similarly, Darwin observed that infants' method of expression for certain emotions was instinctive, as they were able to display emotional expressions they had not themselves yet witnessed. These similarities in morphology and movement are important for the correct interpretation of an emotion. He looked at the functions of facial expression in terms of the utility of expression in the life of the animal and in terms of specific expressions within species. Darwin deduced that some animals communicated feelings of different emotional states with specific facial expressions. He further concluded that this communication was important for the survival of animals in group-dwelling species; the skill to effectively communicate or interpret another animal's feelings and behaviors would be a principal trait in naturally fit species. From Wikipedia, the free encyclopedia. This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. The basic structure of an LSTM, adapted from [ 50 ]. Second, an LSTM supports both fixed-length and variable-length inputs or outputs [ 51 ]. Kahou et al. In this work, the authors presented a complete system for the Emotion Recognition in the Wild EmotiW Challenge [ 52 ], and proved that a hybrid CNN-RNN architecture for a facial expression analysis can outperform a previously applied CNN approach using temporal averaging for aggregation. Kim et al. The spatial image characteristics of the representative expression-state frames are learned using a CNN. In the second part, temporal characteristics of the spatial feature representation in the first part are learned using an LSTM of the facial expression. Chu et al. First, the spatial representations are extracted using a CNN, which is able to reduce person-specific biases caused by handcrafted descriptors e. To model the temporal dependencies, LSTMs are stacked on top of these representations, regardless of the lengths of the input video sequences. Hasani and Mahoor [ 54 ] proposed the 3D Inception-ResNet architecture followed by an LSTM unit that together extracts the spatial relations and temporal relations within the facial images between different frames in a video sequence. Facial landmark points are also used as inputs of this network, emphasizing the importance of facial components rather than facial regions, which may not contribute significantly to generating facial expressions. Graves et al. Jain et al. Initially, this approach subtracts the background and isolates the foreground from the images, and then extracts the texture patterns and the relevant key features of the facial points. The relevant features are then selectively extracted, and an LSTM-CNN is employed to predict the required label for the facial expressions. Commonly, deep learning-based approaches determine features and classifiers by deep neural networks experts, unlike conventional approaches. Deep learning-based approaches extract optimal features with the desired characteristics directly from data using deep convolutional neural networks. However, it is not easy to collect a large amount of training data for the facial emotion under the different conditions enough to learn deep neural networks. Moreover, deep learning-based approaches require more a higher-level and massive computing device than convention approaches to operate training and testing [ 35 ]. Therefore, it is necessary to reduce the computational burden at inference time of deep learning algorithm. Spatial image characteristics of the representative expression-state frames are learned using a CNN. Temporal characteristics of the spatial feature representation in the first part are learned using an LSTM. LSTM unit that together extracts the spatial relations and temporal relations within facial images. Using a recurrent network for temporal dependencies present in the image sequences during classification. Extraction of the texture patterns and the relevant key features of the facial points. Therefore, this hybrid model can learn to recognize and synthesize temporal dynamics for tasks involving sequential images. As shown in Figure 5 , each visual feature determined through a CNN is passed to the corresponding LSTM, and produces a fixed or variable-length vector representation. The outputs are then passed into a recurrent sequence-learning module. Finally, the predicted distribution is computed by applying softmax [ 51 , 53 ]. Overview of the general hybrid deep-learning framework for FER. In the field of FER, numerous databases have been used for comparative and extensive experiments. Traditionally, human facial emotions have been studied using either 2D static images or 2D video sequences. A 2D-based analysis has difficulty handling large pose variations and subtle facial behaviors. The analysis of 3D facial emotions will facilitate an examination of the fine structural changes inherent in spontaneous expressions [ 40 ]. Therefore, this sub-section briefly introduces some popular databases related to FER consisting of 2D and 3D video sequences and still images:. The age range of its subjects is from 18 to 30 years, most of who are female. Image sequences may be analyzed for both action units and prototypic emotions. It provides protocols and baseline results for facial feature tracking, AUs, and emotion recognition. Compound Emotion CE [ 17 ]: CE contains images corresponding to 22 categories of basic and compound emotions for its human subjects females and males, mean age of Most ethnicities and races are included, including Caucasian, Asian, African, and Hispanic. Facial occlusions are minimized, with no glasses or facial hair. Male subjects were asked to shave their faces as cleanly as possible, and all participants were also asked to uncover their forehead to fully show their eyebrows. The database also includes 66 facial landmark points for each image in the database. It was designed for research on 3D human faces and facial expressions, and for the development of a general understanding of human behavior. It contains a total of subjects, 56 females and 44 males, displaying six emotions. There are 25 3D facial emotion models per subject in the database, and a set of 83 manually annotated facial landmarks associated with each model. The JAFFE database contains images of seven facial emotions six basic facial emotions and one neutral posed by ten different female Japanese models. Each image was rated based on six emotional adjectives using 60 Japanese subjects. This database consists of a set of 16, facial images taken under a single light source, and contains 28 distinct subjects for viewing conditions, including nine poses for each of 64 illumination conditions. MMI [ 43 ]: MMI consists of over video sequences and high-resolution still images of 75 subjects. It is fully annotated for the presence of AUs in the video sequences event coding , and partially coded at the frame-level, indicating for each frame whether an AU is in a neutral, onset, apex, or offset phase. It contains a total of video sequences on 28 subjects, both males and females. BP4D-spontanous is a 3D video database that includes a diverse group of 41 young adults 23 women, 18 men with spontaneous facial expressions. The subjects were 18—29 years in age. The facial features were tracked in the 2D and 3D domains using both person-specific and generic approaches. The database promotes the exploration of 3D spatiotemporal features during subtle facial expressions for a better understanding of the relation between pose and motion dynamics in facial AUs, as well as a deeper understanding of naturally occurring facial actions. This database contains images of human emotional facial expressions. The database consists of 70 individuals, each displaying seven different emotional expressions photographed from five different angles. Table 4 shows a summary of these publicly available databases. Provides protocols and baseline results for facial feature tracking, action units, and emotion recognition. Examples of nine representative databases related to FER. Databases a through g support 2D still images and 2D video sequences, and databases h through i support 3D video sequences. Unlike the databases described above, MPI facial expression database [ 60 ] collects a large variety of natural emotional and conversational expressions under the assumption that people understand emotions by analyzing both the conversational expressions as well as the emotional expressions. This database consists of more than 18, samples of video sequences from 10 females and nine male models displaying various facial expressions recorded from one frontal and two lateral views. In recent, other sensors, such as NIR camera, thermal camera, and Kinect sensors, are having interesting of FER researches because visible light image is easily changeable when there are changes in environmental illumination conditions. Natural visible and infrared facial expression USTC-NVIE database [ 32 ] collected both spontaneous and posed expressions of more than subjects simultaneously using a visible and an infrared thermal camera. Facial expressions and emotions database FEEDB is a multimodal database of facial expressions and emotion recorded using Microsoft Kinect sensor. It contains of recordings of 50 persons posing for 33 different facial expressions and emotions [ 33 ]. As described here, various sensors other than the camera sensor are used for FER, but there is a limitation in improving the recognition performance with only one sensor. Therefore, it is predicted that the attempts to increase the FER through the combination of various sensors, will continue in the future. Given the FER approaches, evaluation metrics of the FER approaches are crucial because they provide a standard for a quantitative comparison. In this section, a brief review of publicly available evaluation metrics and a comparison with the benchmark results are provided. Many approaches are used to evaluate the accuracy using two different experiment protocols: First, a subject-independent task splits each database into training and validation sets in a strict subject-independent manner. This task is also called a K-fold cross-validation. The purpose of K-fold cross-validation is to limit problems such as overfitting and provide insight regarding how the model will generalize into an independent unknown dataset [ 61 ]. With the K-fold cross-validation technique, each dataset is evenly partitioned into K folds with exclusive subjects. Then, a model is iteratively trained using K-1 folds and evaluated on the remaining fold, until all subjects are tested. The accuracy is estimated by averaging the recognition rate over K folds. For example, in ten-fold cross-validation adopted for an evaluation, nine folds are used for training, and one fold is used for testing. After this process is performed ten different times, the accuracies of the ten results are averaged and defined as the classifier performance. The second protocol is a cross-database task. In this task, one dataset is used entirely for testing the model, and the remaining datasets listed in Table 4 are used to train the model. The model is iteratively trained using K-1 datasets and evaluated on the remaining dataset repeatedly until all datasets have been tested. The accuracy is estimated by averaging the recognition rate over K datasets in a manner similar to K-fold cross-validation. The evaluation metrics of FER are classified into four methods using different attributes: The precision is the fraction of automatic annotations of emotion i that are correctly recognized. The recall is the number of correct recognitions of emotion i over the actual number of images with emotion i [ 18 ]. The accuracy is the ratio of true outcomes both true positive to true negative to the total number of cases examined. Another metric, the F1-score, is divided into two metrics depending on whether they use spatial or temporal data: Each metric captures different properties of the results. This means that a frame-based F-score has predictive power in terms of spatial consistency, whereas an event-based F-score has predictive power in terms of the temporal consistency [ 62 ]. A frame-based F1-score is defined as. An event-based F1-score is used to measure the emotion recognition performance at the segment level because emotions occur as a temporal signal. ER is the ratio of correctly detected events over the true events, while the EP is the ratio of correctly detected events over the detected events. F1-event considers that there is an event agreement if the overlap is above a certain threshold [ 63 ]. To show a direct comparison between conventional handcrafted-feature-based approaches and deep-learning-based approaches, this review lists public results on the MMI dataset. Table 5 shows the comparative recognition rate of six conventional approaches and six deep-learning-based approaches. Recognition performance with MMI dataset, adapted from [ 11 ]. Sparse representation classifier with LBP features [ 63 ]. Sparse representation classifier with local phase quantization features [ 64 ]. SVM with Gabor wavelet features [ 65 ]. Sparse representation classifier with LBP from three orthogonal planes [ 66 ]. Sparse representation classifier with local phase quantization feature from three orthogonal planes [ 67 ]. Collaborative expression representation CER [ 68 ]. Deep learning of deformable facial action parts [ 69 ]. Joint fine-tuning in deep neural networks [ 48 ]. AU-aware deep networks [ 70 ]. AU-inspired deep networks [ 71 ]. Deeper CNN [ 72 ]. As shown in Table 5 , deep-learning-based approaches outperform conventional approaches with an average of In conventional FER approaches, the reference [ 68 ] has the highest performance than other algorithms..

In Task 2, we used dynamic stimuli in order to extend the measurement of emotion identification to more real life-like situations and to ensure adequate Category emotion expression face facial representation of the final task battery Embretson, Because previous findings predict higher accuracy rates for emotion identification from dynamic stimuli, we implemented intensity manipulations in order to avoid ceiling effects. Hess et al. We generated expression-end-states by morphing intermediate expressions between a neutral and an emotional face.

Mixture ratios for the morphs aimed at Category emotion expression face facial intensity levels by decreasing the proportion of neutral relative to the full emotion expressions from In order to capture the contrast between configural vs. Face inversion strongly impedes holistic processing, Category emotion expression face facial mainly feature-based processing Calder et al. McKelvie indicated an increase of errors and RTs of emotion perception from static faces presented upside-down and similar findings were reported for dynamic stimuli as well Ambadar et al.

The first frame of the video displayed a neutral facial expression that, across the subsequent frames, changed to an emotional facial expression. The videos ended at ms and the peak expression learn more here in the last frame remained on the screen until the categorization was performed.

Emotion label buttons were the same as in the previous task. We varied expression intensity across trials, with one third of the trials for each intensity level. The morphing procedure was similar to the procedure used in previous studies e. First, static pictures were generated by morphing a neutral expression image of a face Category emotion expression face facial with the images of the same person showing one of the 6 basic emotions; mixture ratios were 40, source, or 80 percent of the emotional face.

Second, short video sequences were produced on the basis of a morphed sequence of frames starting from a neutral expression and ending with one of emotional faces generated in the first step. Thus, video sequences were created for all three intensities; this was done separately for two female and two male models. Half of the 72 trials were presented upright and the other presented upside down.

Following the instructions participants completed four practice trials. The experimental trials with varying conditions upright vs. In addition to results for the percent correct scores, we also report unbiased Category emotion expression face facial rates see above. Table 2 summarizes the average performance calculated for both, percent correct and unbiased hit rates the scores are correlated 0.

It seems that the facial expressions click to see more anger used here were particularly heterogeneous.

There were no ceiling effects in any of the indicators. An rmANOVA with factors for emotion expression and expression intensity revealed main effects for both. The rank order of recognizability of different emotional expressions was comparable with Task 1, which used expression composites cf.

Figures 2A,B. Happiness and surprise were recognized the best, followed by anger and disgust, and finally sadness and fear were the most difficult. Scores calculated across all trials within single emotions disregarding the intensity manipulation had acceptable or good psychometric quality. Figure 2. Plots of the rank order of recognizability of the different Category emotion expression face facial categories esteemed in emotion perception task. A Task 1, Identification of Emotion Expression from composite faces; B Task 2, Identification of Emotion Expression of different intensity from upright and inverted dynamic face stimuli; C Task 3, Visual search for faces with corresponding Emotion Category emotion expression face facial of different intensity, error bars Category emotion expression face facial confidence intervals.

Task 3 was inspired by the visual search paradigm often implemented for investigating attention biases to emotional faces e. In general, visual search tasks require the identification of a target object that differs in at least one feature e. In this task, participants had to recognize several target facial expressions that differed from a prevailing emotion expression. Usually, reaction time slopes are inspected as dependent performance variables in visual search tasks.

However, we set no limits on response time and encouraged participants to screen and correct their responses before confirming their choice.

This way we aimed to minimize the influence of visual saliency of different emotions on the search efficiency due to pre-attentive processes Calvo and Nummenmaa, and capture intentional processing instead. This task assessed the ability to discriminate between different emotional facial expressions. The majority Category emotion expression face facial the images displayed one emotional expression surprise, fear, sadness, disgust, or anger Category emotion expression face facial to here as the target expression.

In each trial participants were asked to identify the neutral and emotional expressions. Experimental manipulations incorporated in each trial were: Happiness expressions were not used in this task because performance for smiling faces was assumed to be at ceiling due to pop out effects. The location of target stimuli within the grid was pseudo-randomized. Participants' task was to identify and indicate all distracter expressions by clicking with their mouse a tick box below each stimulus.

The task aimed to implement two levels of difficulty by using target and distracter expressions with low and high intensity. Figure 3. Schematic representation of a trail from Task 3 Visual search for faces with corresponding emotion expression of different intensity.

All stimuli were different images originating from four models two females and two males.

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Intensity level was assessed with the FaceReader software. Based on these intensity levels, check this out were composed of either low or high intense emotion stimuli for targets as well as for distracters within the same trial.

The number of divergent expressions to be identified was distributed uniformly across conditions. There were 40 experimental learn more here administered after three practice trials, which followed the instructions. The accuracies of the multiple answers for a trial are dependent variables.

We applied three different scoring procedures. The first was based on the proportion of correctly recognized targets. This procedure only accounts for the hit rates, disregards false alarms, and can be used to evaluate the detection rate of target facial expressions. For the second, we computed a difference score between the hit-rate and false-alarm rate for each trial.

This score is an indicator of the ability to recognize distracter expressions. Next, we will report proportion correct scores. Table 2 additionally displays average performance based on the d'prime scores. The univariate distributions of emotion-specific performance indicators and the average performance—displayed in Table 2 —suggest substantial individual differences in accuracy measures.

The task design was successful at avoiding ceiling effects frequently observed for recognition performance of prototypical expressions.

This was presumably achieved by using stimuli of varying expression intensity and by the increasing number of distracters across trials. Considering that only eight trials entered the emotion specific scores and that emotional expressions are rather heterogeneous, reliability estimates ranging from 0.

The rank orders of recognizability of the emotion categories were slightly different from those estimated in Task 1 and 2 see Figures 2C,B compared with Figures 2A,B. Surprised faces were recognized the best, as was the case for Task 2. Anger faces were recognized considerably worse than sadness faces. This inconsistency might be due to effects of stimulus sampling. Performance on fear expressions was the poorest. The difficulty manipulation based on high Category emotion expression face facial.

The ratios of the difference between low and high intensity conditions varied across emotions: We conclude that performance indicators derived from this task have acceptable psychometric quality.

Empirical difficulty levels differ across the intended manipulations based on expression intensity and the task revealed a rank order of Category emotion expression face facial similar to other tasks used in this study.

The scoring procedure hardly affected the rank order of persons, allowing the conclusion that different scores derived from this task express the same emotional expression discrimination ability.

It is suggested that the encoding of facial emotion expressions is based on discrete categorical qualitative matching Etcoff and Magee, ; Calder et al. There is evidence that both types of perception are integrated and used complementary Fujimura et al. In this task, we required participants to determine the mixture ratios of two prototypical expressions of emotions. In order to avoid memory-related processes we constructed a simultaneous Category emotion expression face facial task.

We morphed expressions of two emotions along a continuum of 10 mixture ratios. Category emotion expression face facial only morphed continua between adjacent emotions on a so-called emotion hexagon with the sequence happiness-surprise-fear-sadness-disgust-angerwhere proximity of emotions represents potentially stronger confusion between expressions e.

In terms of categorical perception, there should be an advantage in identifying the correct mixture-ratio at the end of a continuum compared with more balanced stimuli in the middle of the continuum between two expression categories Calder et al.

Morphed images were created from two different expressions with theoretically postulated and empirically tested maximal confusion rates Ekman and Friesen, Thus, morphs were created on the following six continua: These morphs were created for Category emotion expression face facial face separately for five female and five male models.

In every trial, two images of the same identity were presented on the upper left and on the upper right side of the screen, where each image displayed a different prototypical emotion expression happiness, surprise, fear, sadness, disgust, and anger. Below these faces, centered on the link, was a single expression morphed from the prototypical faces displayed in the upper part of the screen.

Category emotion expression face facial three faces remained on the screen until participants responded. Participants were asked to estimate the ratio of the morphed photo on Category emotion expression face facial continuous visual analog scale.

Participants were asked to estimate the mixture-ratio of the morph photo as exactly as possible, using the full range of Category emotion expression face facial scale. There were no time limits. Three practice article source preceded 60 experimental trials.

We scored performance accuracy as the average absolute deviation of participants' response from the correct proportion of the mixture between the two parent expressions. Table 2 displays the average overall and emotion specific deviation scores.

Further, it was interesting to investigate whether performance was higher toward the ends of the continua as predicted by categorical accounts of emotional expression perception. A series of two-tailed paired t -tests compared differences between the emotion categories of the parent photo. The correct mixture ratio was better identified in the following combinations: Generally, we expected mixtures of more similar expressions to bias the evaluation of the morphs.

The results are essentially in line with these expectations based on expression similarities. Taken together, the results suggest the deviation scores meet psychometric standards. Performance improved or worsened as predicted by theories of categorical perception. Future research should examine whether expression assignment in Category emotion expression face facial emotions is indicative of the ability to identify prototypical emotion expressions.

This task is a forced choice version of the previously described Task 4 and aims to measure categorical perception of emotional expressions using a further assessment method. Participants were asked to decide whether the morphed expression presented in the upper middle of the screen was more similar to the expression prototype displayed on the lower left or lower right side of the screen.

Stimuli were identical with those used in Task 4, but the sequence of presentation was different. The task design differed from that of Category emotion expression face facial 4 only in that participants were forced to decide whether the expression-mix Category emotion expression face facial was composed of more of the left or more Category emotion expression face facial the right prototypical expression. Response keys were the left and right control keys on the regular computer keyboard, which were marked with colored tape.

The average percentages of correct decisions are given in Table 2. This task was rather easy compared with Tasks 1—3.

The distribution of the scores was, however, not strongly skewed to the right, but rather followed a normal distribution with most of the participants performing within the range of 0. Similarly to Task 4, the rank order of emotion recognizability was not similar to Tasks 1 or 2. Generally, the psychometric properties of this task need improvement and further studies should address the question whether forced-choice expression assignment in emotion-morphs is indicating the same ability factor indicated by the other tasks i.

The following five tasks arguably assess individual differences in memory-related abilities in the domain of facial expressions. All tasks consist of a learning phase for facial expressions and a subsequent retrieval phase that requires recognition or recall of previously learned expressions. The first three memory tasks include an intermediate task between learning and recall of at least three minutes, hence challenging long-term retention. In Task 9 and 10, learning is immediately followed by retrieval.

With this forced-choice SM task we aimed to assess the ability to learn and recognize facial expressions of different intensity. Emotion category, emotion intensity, and learning-set size varied across trials, but face identity was constant within a block of expressions that the participant was asked to learn together. Manipulations of expression intensity within targets, but also between targets and distracters, were used to increase task difficulty. The recognition of expression intensity is also a challenge in everyday life; hence, the expression intensity manipulation is not restricted to psychometric rationales.

We expected hit-rates Category emotion expression face facial decline with increasing ambiguity for less intense targets e. We administered Category emotion expression face facial practice block of trials and four experimental blocks—including four face identities half were females and 18 trials per block.

Each block started by presenting a set of target faces of the same face identity but with different emotion expressions. To-be-learned Category emotion expression face facial were presented simultaneously in a line centered on the screen.

Experimental blocks differed in the number of targets, expressed emotion, expression intensity, and presentation time. Presentation time ranged from 30 to 60 s depending on the number of targets within a block two up to five stimuli. Continue reading expressions of six emotions were used as targets as well as distracters happiness, surprise, anger, fear, sadness, and disgust.

Participants were instructed to remember the combination of both expression and intensity. During a Category emotion expression face facial phase of about three minutes, participants worked on a two-choice RT task they had to decide whether two simultaneous presented number series are the same or different.

Recall was structured as a pseudo-randomized sequence of 18 single images of targets or distracters. Category emotion expression face facial were identical with the previously learned expressions in terms of emotional content and intensity, Category emotion expression face facial different photographs of the same identities were used in order to reduce effects of simple Category emotion expression face facial recognition. Distracters differed from the targets in both expression content and intensity.

Participants were requested to provide a two-choice discrimination decision between learned and distracter expressions on the keyboard. After a response, the next stimulus was presented. The average performance accuracy over all trials and across trials of specific emotion categories is presented in Table 3. Pairwise comparison based on adjusted p -values for simultaneous inference using the Bonferroni-method showed that participants were better at recognizing happiness relative to all other emotions.

Additionally, expressions of anger were significantly better retrieved than surprise, fear, or disgust expressions. There were no additional performance differences due Category emotion expression face facial emotion content. Table 3. Descriptive statistics and reliability estimates of performance accuracy for Category emotion expression face facial emotion memory tasks—across all trials and for single target emotions if applicable.

Intensity manipulation was partly successful: While performance on low intensity stimuli was slightly better than performance on medium intensity stimuli, we believe this effect reflects a Type 1 error and will not replicate in an independent sample.

We recommend using high vs. Reliability estimates are provided in Table 3 and suggest good psychometric properties for the overall task. Reliabilities for emotion-specific trials are acceptable considering the low number of indicators and the heterogeneity of facial emotion expressions in Category emotion expression face facial. In sum, we recommend using two levels of difficulty and the overall performance as indicators of expression recognition accuracy for this task.

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In this delayed recognition memory task, we displayed facial expressions with a frontal view as well as right see more left three-quarter views. We aimed to assess long-term memory bindings between emotion expressions and face orientation. Thus, in order to achieve a correct Category emotion expression face facial, participants needed to store both the emotion expressions and the viewpoints.

This task is based on the premise that remembering content-context Category emotion expression face facial is crucial in everyday socio-emotional interactions. An obvious hypothesis regarding this task is that emotion expressions are recognized more accurately from the frontal view than from the side, because more facial muscles are visible from the frontal view.

On the Category emotion expression face facial side, Matsumoto and Hwang reported that the presentation of emotional expressions in hemi-face profiles did not lower accuracy rates of recognition relative to frontal views.

It is important to note that manipulation of the viewpoint is confounded with manipulating gaze direction in the present task. Adams and Kleck discuss effects of gaze direction on the processing of emotion expressions.

A comparison of Category emotion expression face facial rates between frontal and three-quarter views is therefore interesting. This task includes one practice block and four experimental blocks with each including only one face identity and consisting of 12—16 recall-trials. During the initial learning phase, emotion expressions from different viewpoints were simultaneously presented. The memory set size varied across blocks from four to seven target stimuli.

Targets differed according to the six basic emotions and the three facial perspectives frontal, left, and right profile views.

Presentation time changed depending on the number of read more presented during the learning phase and ranged between 30 and 55 s. Participants were explicitly instructed to memorize the association between expressed emotion and perspective. These images were shown in a pseudo-randomized sequence intermixed with distracters, which differed from the targets in expression, perspective, or both.

Participants were asked to decide whether or not a given image had been shown during the learning phase by pressing one of two buttons on the keyboard. After the Category emotion expression face facial chose their response, the next trial started. Table 3 displays the performance accuracy for this task. The average scores suggest adequate levels of task difficulty—well above guessing probability and below ceiling. Reliability estimates for the emotion specific trials were considerably lower; these estimates might be raised by increasing the number of stimuli per emotion category.

Pairwise comparisons showed that expressions of happiness and surprise were recognized the best and anger Category emotion expression face facial fear were recognized the worst. Viewpoint effects were as expected and contradict the results from Matsumoto and Hwang Expressions were recognized significantly better if the expression was learned with frontal rather than a three quarter view: We recommend using face orientation as a difficulty manipulation and the overall performance across trials as indicator of expression recognition.

With this task, we intended to assess recognition performance of mixed—rather than prototypical—facial expressions. It was not our aim to test theories that Category emotion expression face facial combinations of emotions that result in complex affect expressions, such as contempt, which is proposed as a mixture of anger and disgust or disappointment, which is proposed as a combination of surprise and sadness cf.

Plutchik, Instead, we aimed to use compound emotion expressions to assess the ability Category emotion expression face facial recognize less prototypical, and to some extent more real-life, expressions. Furthermore, these expressions are not as easy to label as the basic emotion expressions parenting the mixed expressions.

Therefore, for the mixed emotions of the present task we expect a smaller contribution of verbal encoding to task performance, as has been reported for face recognition memory Category emotion expression face facial basic emotions Nakabayashi and Burton, We used nine different combinations of six basic emotions Plutchik, Within each block of trials, the images used for morphing mixed expressions Category emotion expression face facial from a single identity. Across blocks, sex of the identities was balanced.

There were four experimental blocks preceded by a practice block. The number of stimuli to be learned ranged from two targets in Block 1 to five targets in Block 4. The presentation time of the targets during the learning period changed depending on the number of targets displayed, ranging from 30 to 60 s.

Across blocks, 11 targets showed morphed mixture ratios of During the learning phase, stimuli were presented simultaneously on the screen. During a delay period of approximately three minutes, participants answered a subset of questions from the Trait Meta Mood Scale Salovey et al. At retrieval, participants saw a pseudo-randomized sequence Category emotion expression face facial images displaying mixed expressions.

Half of the trials were learned images. The other trials differed from the learned targets in the expression mixture, in the mixture ratio, or both. There were 56 recall trials in this task. The same scoring procedures were used as in Task 7. The average performance over all trials see Table 3 was continue reading above chance.

Different scoring procedures hardly affected the rank order of individuals within the sample; the proportion correct scores were highly correlated with the d'prime scores click here. Reliability Category emotion expression face facial suggest good psychometric quality.

Further studies are needed to investigate whether learning Category emotion expression face facial recognizing emotion-morphs are tapping the same ability factor as learning and recognizing prototypical expressions of emotion. Because expectations on mean differences at recognizing expression morphs are difficult to derive from a theoretical point of view, we only consider the psychometric quality of the overall score for this Category emotion expression face facial.

Memory span paradigms are frequently used measures of primary memory. The present task was designed as a serial cued memory task for emotion expressions of different intensity.

Because recognition was required in the serial order of the stimuli displayed at learning, the sequence read article presentation served as a temporal-context for memorizing facial expressions. We used FaceReader see above to score intensity levels of the Category emotion expression face facial chosen for this task.

We used three male and four female identities throughout the task, with one identity per block. The task began with a practice block followed by seven experimental blocks of trials. Each block started with a sequence of facial expressions happiness, surprise, fear, sadness, disgust, and angerpresented one at a time, and was followed immediately by the retrieval phase. The sequence of targets at retrieval was the same as the memorized sequence.

Participants were suggested to use the serial position as memory cue. Number of trials within a sequence varied between three and six.

Most of the targets 25 of 33 images and distracters 37 of 54 images displayed high intensity prototypical expressions. During the learning phase stimulus presentation was fixed to ms, followed by a blank inter-stimulus interval of another ms. The position of the target in this matrix varied across trials.

Distracters within a trial differed from the target in its emotional expression, intensity, or both. Participants indicated the learned expression via mouse click on the target image. Table 3 provides performance click here reliability estimates. Average performance ranged between 0.

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Russell; J. Fernandez Dols The psychology of facial expression 1 ed. Cambridge University Press. Cognition and Emotion. Psychological Bulletin. Physiology of behavior 10th ed.

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Current Opinion in Neurobiology. Frontiers in Psychology. Journal of Cognitive Psychology. The Psychology of Emotions: The Allure of Human Face. In the Company of Others: An Introduction to Communication.

Xxx Colejial Watch PORN Movies Global fucking. The following five tasks arguably assess individual differences in memory-related abilities in the domain of facial expressions. All tasks consist of a learning phase for facial expressions and a subsequent retrieval phase that requires recognition or recall of previously learned expressions. The first three memory tasks include an intermediate task between learning and recall of at least three minutes, hence challenging long-term retention. In Task 9 and 10, learning is immediately followed by retrieval. With this forced-choice SM task we aimed to assess the ability to learn and recognize facial expressions of different intensity. Emotion category, emotion intensity, and learning-set size varied across trials, but face identity was constant within a block of expressions that the participant was asked to learn together. Manipulations of expression intensity within targets, but also between targets and distracters, were used to increase task difficulty. The recognition of expression intensity is also a challenge in everyday life; hence, the expression intensity manipulation is not restricted to psychometric rationales. We expected hit-rates to decline with increasing ambiguity for less intense targets e. We administered one practice block of trials and four experimental blocks—including four face identities half were females and 18 trials per block. Each block started by presenting a set of target faces of the same face identity but with different emotion expressions. To-be-learned stimuli were presented simultaneously in a line centered on the screen. Experimental blocks differed in the number of targets, expressed emotion, expression intensity, and presentation time. Presentation time ranged from 30 to 60 s depending on the number of targets within a block two up to five stimuli. Facial expressions of six emotions were used as targets as well as distracters happiness, surprise, anger, fear, sadness, and disgust. Participants were instructed to remember the combination of both expression and intensity. During a delay phase of about three minutes, participants worked on a two-choice RT task they had to decide whether two simultaneous presented number series are the same or different. Recall was structured as a pseudo-randomized sequence of 18 single images of targets or distracters. Targets were identical with the previously learned expressions in terms of emotional content and intensity, but different photographs of the same identities were used in order to reduce effects of simple image recognition. Distracters differed from the targets in both expression content and intensity. Participants were requested to provide a two-choice discrimination decision between learned and distracter expressions on the keyboard. After a response, the next stimulus was presented. The average performance accuracy over all trials and across trials of specific emotion categories is presented in Table 3. Pairwise comparison based on adjusted p -values for simultaneous inference using the Bonferroni-method showed that participants were better at recognizing happiness relative to all other emotions. Additionally, expressions of anger were significantly better retrieved than surprise, fear, or disgust expressions. There were no additional performance differences due to emotion content. Table 3. Descriptive statistics and reliability estimates of performance accuracy for all emotion memory tasks—across all trials and for single target emotions if applicable. Intensity manipulation was partly successful: While performance on low intensity stimuli was slightly better than performance on medium intensity stimuli, we believe this effect reflects a Type 1 error and will not replicate in an independent sample. We recommend using high vs. Reliability estimates are provided in Table 3 and suggest good psychometric properties for the overall task. Reliabilities for emotion-specific trials are acceptable considering the low number of indicators and the heterogeneity of facial emotion expressions in general. In sum, we recommend using two levels of difficulty and the overall performance as indicators of expression recognition accuracy for this task. In this delayed recognition memory task, we displayed facial expressions with a frontal view as well as right and left three-quarter views. We aimed to assess long-term memory bindings between emotion expressions and face orientation. Thus, in order to achieve a correct response, participants needed to store both the emotion expressions and the viewpoints. This task is based on the premise that remembering content-context bindings is crucial in everyday socio-emotional interactions. An obvious hypothesis regarding this task is that emotion expressions are recognized more accurately from the frontal view than from the side, because more facial muscles are visible from the frontal view. On the other side, Matsumoto and Hwang reported that the presentation of emotional expressions in hemi-face profiles did not lower accuracy rates of recognition relative to frontal views. It is important to note that manipulation of the viewpoint is confounded with manipulating gaze direction in the present task. Adams and Kleck discuss effects of gaze direction on the processing of emotion expressions. A comparison of accuracy rates between frontal and three-quarter views is therefore interesting. This task includes one practice block and four experimental blocks with each including only one face identity and consisting of 12—16 recall-trials. During the initial learning phase, emotion expressions from different viewpoints were simultaneously presented. The memory set size varied across blocks from four to seven target stimuli. Targets differed according to the six basic emotions and the three facial perspectives frontal, left, and right profile views. Presentation time changed depending on the number of stimuli presented during the learning phase and ranged between 30 and 55 s. Participants were explicitly instructed to memorize the association between expressed emotion and perspective. These images were shown in a pseudo-randomized sequence intermixed with distracters, which differed from the targets in expression, perspective, or both. Participants were asked to decide whether or not a given image had been shown during the learning phase by pressing one of two buttons on the keyboard. After the participants chose their response, the next trial started. Table 3 displays the performance accuracy for this task. The average scores suggest adequate levels of task difficulty—well above guessing probability and below ceiling. Reliability estimates for the emotion specific trials were considerably lower; these estimates might be raised by increasing the number of stimuli per emotion category. Pairwise comparisons showed that expressions of happiness and surprise were recognized the best and anger and fear were recognized the worst. Viewpoint effects were as expected and contradict the results from Matsumoto and Hwang Expressions were recognized significantly better if the expression was learned with frontal rather than a three quarter view: We recommend using face orientation as a difficulty manipulation and the overall performance across trials as indicator of expression recognition. With this task, we intended to assess recognition performance of mixed—rather than prototypical—facial expressions. It was not our aim to test theories that postulate combinations of emotions that result in complex affect expressions, such as contempt, which is proposed as a mixture of anger and disgust or disappointment, which is proposed as a combination of surprise and sadness cf. Plutchik, Instead, we aimed to use compound emotion expressions to assess the ability to recognize less prototypical, and to some extent more real-life, expressions. Furthermore, these expressions are not as easy to label as the basic emotion expressions parenting the mixed expressions. Therefore, for the mixed emotions of the present task we expect a smaller contribution of verbal encoding to task performance, as has been reported for face recognition memory for basic emotions Nakabayashi and Burton, We used nine different combinations of six basic emotions Plutchik, Within each block of trials, the images used for morphing mixed expressions were from a single identity. Across blocks, sex of the identities was balanced. There were four experimental blocks preceded by a practice block. The number of stimuli to be learned ranged from two targets in Block 1 to five targets in Block 4. The presentation time of the targets during the learning period changed depending on the number of targets displayed, ranging from 30 to 60 s. Across blocks, 11 targets showed morphed mixture ratios of During the learning phase, stimuli were presented simultaneously on the screen. During a delay period of approximately three minutes, participants answered a subset of questions from the Trait Meta Mood Scale Salovey et al. At retrieval, participants saw a pseudo-randomized sequence of images displaying mixed expressions. Half of the trials were learned images. The other trials differed from the learned targets in the expression mixture, in the mixture ratio, or both. There were 56 recall trials in this task. The same scoring procedures were used as in Task 7. The average performance over all trials see Table 3 was well above chance. Different scoring procedures hardly affected the rank order of individuals within the sample; the proportion correct scores were highly correlated with the d'prime scores 0. Reliability estimates suggest good psychometric quality. Further studies are needed to investigate whether learning and recognizing emotion-morphs are tapping the same ability factor as learning and recognizing prototypical expressions of emotion. Because expectations on mean differences at recognizing expression morphs are difficult to derive from a theoretical point of view, we only consider the psychometric quality of the overall score for this task. Memory span paradigms are frequently used measures of primary memory. The present task was designed as a serial cued memory task for emotion expressions of different intensity. Because recognition was required in the serial order of the stimuli displayed at learning, the sequence of presentation served as a temporal-context for memorizing facial expressions. We used FaceReader see above to score intensity levels of the stimuli chosen for this task. We used three male and four female identities throughout the task, with one identity per block. The task began with a practice block followed by seven experimental blocks of trials. Each block started with a sequence of facial expressions happiness, surprise, fear, sadness, disgust, and anger , presented one at a time, and was followed immediately by the retrieval phase. The sequence of targets at retrieval was the same as the memorized sequence. Participants were suggested to use the serial position as memory cue. Number of trials within a sequence varied between three and six. Most of the targets 25 of 33 images and distracters 37 of 54 images displayed high intensity prototypical expressions. During the learning phase stimulus presentation was fixed to ms, followed by a blank inter-stimulus interval of another ms. The position of the target in this matrix varied across trials. Distracters within a trial differed from the target in its emotional expression, intensity, or both. Participants indicated the learned expression via mouse click on the target image. Table 3 provides performance and reliability estimates. Average performance ranged between 0. Reliability estimates for the entire task are acceptable; reliability estimates for the emotion-specific trials were low; increasing the number of trials could improve reliabilities for the emotion-specific trials. We therefore recommend the overall percentage correct score as a psychometrically suitable measure of individual differences of primary memory for facial expressions. The task was to quickly detect the particular pairs and to memorize them in conjunction with their spatial arrangement on the screen. Successful detection of the pairs requires perceptual abilities. During retrieval, one expression was automatically disclosed and participants had to indicate the location of the corresponding expression. Future work might decompose perceptual and mnestic demands of this task in a regression analysis. At the beginning of a trial block several expressions initially covered with a card deck appeared as a matrix on the screen. During the learning phase, all expressions were automatically disclosed and participants were asked to detect expression pairs and to memorize their location. Then, after several seconds, the learning phase was stopped by the program, and again the cards were displayed on the screen. Next, one image was automatically disclosed and participants indicated the location of the corresponding expression with a mouse click. After the participant's response, the clicked image was revealed and feedback was given by encircling the image in green correct or red incorrect. Two seconds after the participant responded, the two images were again masked with the cards, and the next trial started by program flipping over another card to reveal a new image. Figure 4 provides a schematic representation of the trial sequence within an experimental block. Figure 4. Schematic representation of a trail block from Task 10 Memory for facial expression of emotion. Following the practice block, there were four experimental blocks of trials. Expression matrices included three one block , six one block , and nine two blocks pairs of expressions that were distributed pseudo-randomized across the lines and columns. Presentation time for learning depended on the memory set size: Within each block each image pair was used only once, resulting in 27 responses, representing the total number of trials for this task. The average proportion of correctly identified emotion pairs and reliability estimates, are summarized in Table 3. Similar to Task 9, guessing probability is much lower than 0. Reliability is also good. Due to the low number of trials within one emotion category, these reliabilities are rather poor, but could be increased by including additional trials. Pairs of happiness, surprise, anger, and fear expressions were remembered the best and sadness was remembered the worst. In the current version, we recommend the overall score as a psychometrically suitable performance indicator of memory for emotional expressions. We also developed speed indicators of emotion perception and emotion recognition ability following the same rationale as described by Herzmann et al. Tasks that are so easy that the measured accuracy levels are at ceiling allow us to gather individual differences in performance speed. Therefore, for the following tasks we used stimuli with high intensity prototypical expressions for which we expected recognition accuracy rates to be at or close to ceiling above 0. Like the accuracy tasks described above, the speed tasks were intended to measure either emotion perception three tasks or emotion recognition three tasks. Below we describe the six speed tasks and report results on their psychometric properties. Recognizing expressions from different viewpoints is a crucial socio-emotional competence relevant for everyday interaction. Here, we aimed to assess the speed of perceiving emotion expressions from different viewpoints by using a discrimination task with same-different choices. Two same-sex images with different facial identities were presented next to each other. One face was shown with a frontal view and the other in three-quarter view. Both displayed one of the six prototypical emotion expressions. Participants were asked to decide as fast and accurately as possible whether the two persons showed the same or different emotion expressions by pressing one of two marked keys on the keyboard. There was no time limit on the presentation. Participants' response started the next trial, after the presentation of a 1. Trials were pseudo-randomized in sequence and were balanced for expression match vs. To ensure high accuracy rates, confusable expressions according to the hexagon model Sprengelmeyer et al. There was a practice block of six trials with feedback. There were 31 experimental trials. Each of the six basic emotions occurred in match and mismatch trials. Average accuracies and RTs, along with average inverted latency see general description of scoring procedures above are presented in Table 4. As required for speed tasks, accuracy rates were at ceiling. RTs and inverted latencies showed that participants needed about two seconds on average to correctly match the two facial expressions presented in the frontal vs. Bonferroni-adjusted pairwise comparisons indicate the strongest difference in performance between matching emotion expressions occurred between happiness compared to all other emotions. Other statistically significant, but small effects indicated that performance matching surprise, fear, and anger expressions was faster than performance matching sadness and disgust. Reliability estimates are excellent for the overall score and acceptable for the emotion specific trials. However, happiness, surprise, and fear expressions were less frequently used in this task. Reliabilities of emotion-specific scores could be increased by using more trials in future applications. Table 4. Descriptive statistics and reliability estimates of performance speed for all speed measures of emotion perception—across all trials and for single target emotions. This task is a revision of the classic Odd-Man-Out task Frearson and Eysenck, , where several items are shown simultaneously of which one—the odd-man-out—differs from the others. Participants' task is to indicate the location of the odd-man-out. The emotion-expression version of the task—as implemented by Herzmann et al. Three faces of different identities but of the same sex , each displaying an emotion expression, were presented simultaneously in a row on the screen. The face in the center displayed the reference emotion from which either the left or right face differed in expression, whereas the remaining third face displayed the same emotion. Participants had to locate the divergent stimulus odd-man-out by pressing a key on the corresponding side. The next trial started after a 1. Again, we avoided combining highly confusable expressions of emotions in the same trial to ensure high accuracy rates Sprengelmeyer et al. Five practice trials with feedback and 30 experimental trials were administered in pseudo-randomized order. Each emotion occurred as both a target and as a distracter. Table 4 displays relevant results for this task. Throughout, accuracy rates were very high for all performance indicators, demonstrating the task to be a measure of performance speed. On average, participants needed about 2 s to detect the odd-man-out. Differences mainly occurred between happiness and all other expressions. In spite of the small number of trials per emotion category 5 , reliability estimates of the overall score based on inverted latencies are excellent and good for all emotion specific scores. We conclude that the overall task and emotion specific trial scores have good psychometric quality. The purpose of this task is to measure the speed of the visual search process see Task 3 involved in identifying an expression belonging to an indicated expression category. Here, an emotion label, a targeted emotional expression, and three mismatching alternative expressions, were presented simultaneously on the screen. The number of distracters was low in order to minimize task difficulty. Successful performance on this task requires a correct link of the emotion label and the facially expressed emotion and an accurate categorization of the expression to the appropriate semantic category. The name of one of the six basic emotions was printed in the center of the screen. The emotion label was surrounded in horizontal and vertical directions by four different face identities of the same sex all displaying different emotional expressions. Participants were asked to respond with their choice by using the arrow-keys on the number block of a regular keyboard. There were two practice trials at the beginning. Then, each of the six emotions was used eight times as a target in a pseudorandom sequence of 48 experimental trials. There were no time limits for the response, but participants were instructed to be as fast and accurate as possible. The ISI was ms. Average performance, as reflected by the three relevant scores for speed indicators, are depicted in Table 4. Accuracy rates were at ceiling. Expressions of happiness and surprise were detected the fastest, followed by disgust and anger, and finally sadness and fear. Reliability estimates were excellent for the overall score and good for emotion specific performance scores. All results substantiate that the scores derived from Task 12 reflect the intended difficulty for speed tasks and have good psychometric properties. In the n -back paradigm, a series of different pictures is presented; the task is to judge whether a given picture has been presented n pictures before. It has been traditionally used to measure working memory e. The 1 -back condition requires only minimal effort on storage and processing in working memory. Therefore, with the 1 -back task using emotion expressions we aimed to assess recognition speed of emotional expressions from working memory and expected accuracy levels to be at ceiling. We administered a 1 -back task with one practice block and four experimental blocks of trials. Each experimental block consisted of a sequence of 24 different images originating from the same identity displaying all six facial emotional expressions. Participants were instructed to judge whether the emotional expression of each image was the same as the expression presented in the previous trial. The two-choice response was given with a left or right key for mismatches and matches, respectively on a standard keyboard. The next trial started after the participant provided their response, with a fixation cross presented on a blank screen for ms in between trials. Response time was not limited by the experiment. All basic emotion expressions were presented as targets in at least one experimental block. Target and distracters were presented at a ratio of 1: Table 5 summarizes the average accuracies, RTs, and inverted latencies. As expected, accuracies were at ceiling. Participants were on average able to correctly respond to more than one trial per second. Reliability estimates were excellent for the overall task and acceptable for emotion specific latency scores given the low number of trials for an emotion category. These results suggest that Task 14 is a psychometrically sound measure of emotion recognition speed from faces. Table 5. Mean accuracy, reaction times in ms and reliability estimates of performance speed for all speed measures of emotion memory—across all trials and for single target emotions if applicable. The present task was inspired by the Delayed Non-Matching paradigm implemented for face identity recognition by Herzmann et al. This task requires the participant to store and maintain a memory of each emotion expression; the images are presented during the learning phase for a short period of time and during the experimental trials the images have to be recollected from the visual primary memory and compared with a novel facial expression. Because the task requires a short maintenance time for a single item in the absence of interfering stimuli, we expect the task to show accuracy rates at ceiling and to measure short-term recognition speed. A facial expression of happiness, surprise, fear, sadness, disgust, or anger was presented for 1 second. Following a delay of 4 s ms mask; ms blank screen the same emotion expression was presented together with a different facial expression. Depending on where the new distracter expression was presented, participants had to press a left or right response-key on a standard keyboard in order to indicate the distractor facial expression. In each trial we used three different identities of the same sex. During the 36 experimental trials, expressions belonging to each emotion category had to be encoded six times. There were three practice trials. Results are summarized in Table 5. Ekman 's work on facial expressions had its starting point in the work of psychologist Silvan Tomkins. Ekman showed that facial expressions of emotion are not culturally determined, but universal across human cultures. To demonstrate his universality hypothesis, Ekman ran a test on a group of the South Fore people of New Guinea , a pre-industrial culture that was isolated from the West. The experiment participants were told brief stories about emotional events happiness, sadness, anger, fear, surprise, and disgust. After each story, they were asked to select the matching facial expression from an array of three faces. Children selected from an array of only two faces, and their results were similar to the adults'. Subsequent cross-cultural studies found similar results. Both sides of this debate agree that the face expresses emotion. One argument against the evidence presented in support of the universality hypothesis is that the method typically used to demonstrate universality inflates recognition scores. The three main factors are the following:. Darwin argued that the expression of emotions has evolved in humans from animal ancestors, who would have used similar methods of expression. Darwin believed that expressions were unlearned and innate in human nature and were therefore evolutionarily significant for survival. He compiled supporting evidence from his research on different cultures, on infants, and in other animal species. Cross-cultural studies had shown that there are similarities in the way emotions are expressed across diverse cultures, but studies have even shown that there are similarities between species in how emotions are expressed. Research has shown that chimpanzees are able to communicate many of the same facial expressions as humans through the complex movements of the facial muscles. In fact, the facial cues were so similar that Ekman's Facial Action Coding System could be applied to the chimps in evaluating their expressions. Similarly, Darwin observed that infants' method of expression for certain emotions was instinctive, as they were able to display emotional expressions they had not themselves yet witnessed. These similarities in morphology and movement are important for the correct interpretation of an emotion. He looked at the functions of facial expression in terms of the utility of expression in the life of the animal and in terms of specific expressions within species. Darwin deduced that some animals communicated feelings of different emotional states with specific facial expressions. He further concluded that this communication was important for the survival of animals in group-dwelling species; the skill to effectively communicate or interpret another animal's feelings and behaviors would be a principal trait in naturally fit species. From Wikipedia, the free encyclopedia. This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. Find sources: See also: Face perception. Eye contact. Facial Expression of Emotion: From Theory to Application. Fridlund Human facial expression 1 ed. San Diego: Academic Press. Russell; J. Fernandez Dols The psychology of facial expression 1 ed. Cambridge University Press. Cognition and Emotion. Psychological Bulletin. Yet, configural cues alone are not sufficient to create an impressive, lasting effect. Other shape changes are needed. For example, the curvature of the mouth in joy or the opening of the eyes—showing additional sclera—in surprise. Note how the surprise-looking face in Figure 4 appears to also express disinterest or sleepiness. Wide-open eyes would remove these perceptions. But this can only be achieved with a shape change. Hence, our face spaces should include both, configural and shape features. It is important to note that configural features can be obtained from an appropriate representation of shape. Expressions such as fear and disgust seem to be mostly if not solely based on shape features, making recognition less accurate and more susceptible to image manipulation. We have previously shown Neth and Martinez, that configural cues are amongst the most discriminant features in a classical Procrustes shape representation, which can be made invariant to 3D rotations of the face Hamsici and Martinez, a. Thus, each of the six categories of emotion happy, sad, surprise, angry, fear and disgust is represented in a shape space given by classical statistical shape analysis. First the face and the shape of the major facial components are automatically detected. This includes delineating the brows, eyes, nose, mouth and jaw line. The shape is then sample with d equally spaced landmark points. The mean center of mass of all the points is computed. The 2 d -dimensional shape feature vector is given by the x and y coordinates of the d shape landmarks subtracted by the mean and divided by its norm. This provides invariance to translation and scale. The dimensions of each emotion category can now be obtained with the use of an appropriate discriminant analysis method. We use the algorithm defined by Hamsici and Martinez because it minimizes the Bayes classification error. As an example, the approach detailed in this section identifies the distance between the brows and mouth and the width of the face as the two most important shape features of anger and sadness. It is important to note that, if we reduce the computational spaces of anger and sadness to 2-dimensions, they are almost indistinguishable. Thus, it is possible that these two categories are in fact connected by a more general one. This goes back to our question of the number of basic categories used by the human visual system. The face space of anger and sadness is illustrated in Figure 5 , where we have also plotted the feature vectors of the face set of Ekman and Friesen We also plot the images of anger and sadness of Ekman and Friesen In dashed are simple linear boundaries separating angry and sad faces according to the model. This continuous model is further illustrated in b. Note that, in the proposed computational model, the face space defining sadness corresponds to the right-bottom quadrant, while that of anger is given by the left-top quadrant. As in the above, we can use the shape space defined above to find the two most discriminant dimensions separating each of the six categories listed earlier. The resulting face spaces are shown in Figure 6. In each space, a simple linear classifier in these spaces can successfully classify each emotion very accurately. To test this, we trained a linear support vector machine Vapnik, and use the leave-one-out test on the data set of images of Ekman and Friesen Of course, adding additional dimensions in the feature space and using nonlinear classifiers can readily achieve perfect classification i. The important point from these results is to note that simple configural features can linearly discriminate most of the samples in each emotion. These features are very robust to image degradation and are thus ideal for recognition in challenging environments e. Shown in the above are the six feature spaces defining each of the six basic emotion categories. A simple linear Support Vector Machine SVM can achieve high classifica-tion accuracies; where we have used a one-versus-all strategy to construct each classifier and tested it using the leave-one-out strategy. Here, we only used two features dimensions for clarity of presentation. Higher accuracies are obtained if we include additional dimensions and training samples. As seen thus far, human perception is extremely tuned to small configural and shape changes. If we are to develop computer vision and machine learning systems that can emulate this capacity, the real problem to be addressed by the community is that of precise detection of faces and facial features Ding and Martinez, Classification is less important, since this is embedded in the detection process; that is, we want to precisely detect changes that are important to recognize emotions. Most computer vision algorithms defined to date provide, however, inaccurate detections. One classical approach to detection is template matching. In this approach, we first define a template e. This template is learned from a set of sample images; for example, estimating the distribution or manifold defining the appearance pixel map of the object Yang et al. Detection of the object is based on a window search. That is, the learned template is compared to all possible windows in the image. If the template and the window are similar according to some metric, then the bounding box defining this window marks the location and size scale of the face. The major drawback of this approach is that it yields imprecise detections of the learned object, because a window of an non-centered face is more similar to the learned template than a window with background say, a tree. An example of this result is shown in Figure 7. A solution to the above problem is to learn to discriminate between non-centered windows of the objects and well centered ones Ding and Martinez, In this alternative, a non-linear classifier or some density estimator is employed to discriminate the region of the feature space defining well-centered windows of the objects and non-centered ones. This features versus context idea is illustrated in Figure 8. This approach can be used to precisely detect faces, eyes, mouth, or any other facial feature where there is a textural discrimination between it and its surroundings. Figure 9 shows some sample results of accurate detection of faces and facial features with this approach. The idea behind the features versus context approach is to learn to discriminate between the feature we wish to detect e. This approach eliminates the classical overlapping of multiple detections around the object of interest at multiple scales. At the same time, it increases the accuracy of the detection because we are moving away from poor detections and toward precise ones. Precise detections of faces and facial features using the algorithm of Ding and Martinez, The same features versus context idea can be applied to other detection and modeling algorithms, such as Active Appearance Models AAM Cootes et al. One obvious limitation is that the learned model is linear. A solution to this problem is to employ a kernel map. Kernel PCA is one option. Once we have introduced a kernel we can move one step further and use it to address additional issues of interest. A first capability we may like to add to a AAM is the possibility to work with three-dimensions. The second could be to omit the least-squares iterative nature of the Procrustes alignment required in most statistical shape analysis methods such as AAM. RIK add yet another important advantage to shape analysis: Thus, once the shape is been mapped to the RIK space, objects e. By now we know that humans are very sensitive to small changes. But we do not yet know how sensitive or accurate. Of course, it is impossible to be pixel accurate when marking the boundaries of each facial feature, because edges blur over several pixels. This can be readily observed by zooming in the corner of an eye. To estimate the accuracy of human subjects, we performed the following experiment. First, we designed a system that allows users to zoom in at any specified location to facilitate delineation of each of the facial features manually. Second, we asked three people herein referred to as judges to manually delineate each of the facial components of close to 4, images of faces. Third, we compared the markings of each of the three judges. The within-judge variability was on average 3. This gives us an estimate of the accuracy of the manual detections. The average error of the algorithm of Ding and Martinez is 7. Thus, further research is needed to develop computer vision algorithms that can extract even more accurate detection of faces and its components. Another problem is what happens when the resolution of the image diminishes. Humans are quite robust to these image manipulations Du and Martinez, One solution to this problem is to use manifold learning. In particular, we wish to define a non-linear mapping f. This is illustrated in Figure That is, given enough sample images and their shape feature vectors described in the preceding section, we need to find the function which relates the two. This can be done, for example, using kernel regression methods Rivera and Martinez, One of the advantages of this approach is that this function can be defined to detect shape from very low resolution images or even under occlusions. Example detections using this approach are shown in Figure Manifold learning is ideal for learning mappings between face object images and their shape description vectors. Shape detection examples at different resolutions. Note how the shape estimation is almost as good regardless of the resolution of the image. Recent advances in non-rigid structure from motion allow us to recover very accurate reconstructions of both the shape and the motion even under occlusion. A recent approach resolves the nonlinearity of the problem using kernel mappings Gotardo and Martinez, b. Combining the two approaches to detection defined in this section should yield even more accurate results in low-resolution images and under occlusions or other image manipulations. We hope that more research will be devoted to this important topic in face recognition. The approaches defined in this section are a good start, but much research is needed to make these systems comparable to human accuracies. We argue that research in machine learning should address these problems rather than the typical classification one. A first goal is to define algorithms that can detect face landmarks very accurately even at low resolutions. Kernel methods and regression approaches are surely good solutions as illustrated above. But more targeted approaches are needed to define truly successful computational models of the perception of facial expressions of emotion. In the real world, occlusions and unavoidable imprecise detections of the fiducial points, among others, are known to affect recognition Torre and Cohn, ; Martinez, Additionally, some expressions are, by definition, ambiguous. Most importantly though seems to be the fact that people are not very good at recognizing facial expressions of emotion even under favorable condition Du and Martinez, Humans are very robust at detection joy and surprise from images of faces; regardless of the image conditions or resolution. However, we are not as good at recognizing anger and sadness and are worst at fear and disgust. The above results suggest that there could be three groups of expressions of emotion. The first group is intended for conveying emotions to observers. These expressions have evolved a facial construct i. Example expressions in this group are happiness and surprise. A computer vision system—especially a HCI—should make sure these expressions are accurately and robustly recognized across image degradation. Therefore, we believe that work needs to be dedicated to make systems very robust when recognizing these emotions. The second group of expressions e. A computer vision system should recognize these expressions in good quality images, but can be expected to fail as the image degrades due to resolution or other image manipulations. An interesting open question is to determine why this is the case and what can be learned about human cognition from such a result. Therefore, many current FER algorithms are still being used in embedded systems, including smartphones. The remainder of this paper is organized as follows. In Section 2 , conventional FER approaches are described along with a summary of the representative categories of FER systems and their main algorithms. In Section 3 , advanced FER approaches using deep-learning algorithms are presented. In Section 4 and Section 5 , a brief review of publicly available FER database and evaluation metrics with a comparison with benchmark results are provided. Finally, Section 6 offers some concluding remarks and discussion of future work. For automatic FER systems, various types of conventional approaches have been studied. The commonality of these approaches is detecting the face region and extracting geometric features, appearance features, or a hybrid of geometric and appearance features on the target face. For the geometric features, the relationship between facial components is used to construct a feature vector for training [ 22 , 23 ]. Ghimire and Lee [ 23 ] used two types of geometric features based on the position and angle of 52 facial landmark points. First, the angle and Euclidean distance between each pair of landmarks within a frame are calculated, and second, the distance and angles are subtracted from the corresponding distance and angles in the first frame of the video sequence. For the classifier, two methods are presented, either using multi-class AdaBoost with dynamic time warping, or using a SVM on the boosted feature vectors. As an example of using global features, Happy et al. Although this method is implemented in real time, the recognition accuracy tends to be degraded because it cannot reflect local variations of the facial components to the feature vector. Unlike a global-feature-based approach, different face regions have different levels of importance. Ghimire et al. Important local regions are determined using an incremental search approach, which results in a reduction of the feature dimensions and an improvement in the recognition accuracy. For hybrid features, some approaches [ 18 , 27 ] have combined geometric and appearance features to complement the weaknesses of the two approaches and provide even better results in certain cases. In video sequences, many systems [ 18 , 22 , 23 , 28 ] are used to measure the geometrical displacement of facial landmarks between the current frame and previous frame as temporal features, and extracts appearance features for the spatial features. The main difference between FER for still images and for video sequences is that the landmarks in the latter are tracked frame-by-frame and the system generates new dynamic features through displacement between the previous and current frames. Similar classification algorithms are then used in the video sequences, as described in Figure 1. To recognize micro-expression, high speed camera is used to capture video sequences of the face. Polikovsky et al. This study divides face regions into specific regions, and then 3D-Gradients orientation histogram is generated from the motion in each region for FER. Apart from FER of 2D images, 3D and 4D dynamic 3D recordings are increasingly used in expression analysis research because of the problems presented in 2D images caused by inherent variations in pose and illumination. One thing to note in 3D is that dynamic and static system are very different because of the nature of data. Static systems extract feature from statistical models such as deformable model, active shape model, analysis of 2D representations, and distance-based features. In contrast, dynamic systems utilize 3D image sequences for analysis of facial expressions such as 3D motion-based features. For FER, 3D images also use the similar conventional classification algorithms [ 29 , 30 ]. Some researchers [ 31 , 32 , 33 , 34 , 35 ] have tried to recognize facial emotions using infrared images instead of visible light spectrum VIS image because visible light VIS image is variable according to the status of illumination. Zhao et al. Shen et al. This study uses local movements within the face area as the feature and recognized facial expressions using relations between particular emotions. To role of AAM is to adjust shape and texture model in a new face, when there is variation of shape and texture comparing to the training result. Wei et al. This study extracts facial feature points vector by face tracking algorithm using captured sensor data and recognize six facial emotions by random forest algorithm. Commonly, conventional approaches determine features and classifiers by experts. For feature extraction, many well-known handcrafted feature, such as HoG, LBP, distance and angle relation between landmarks are used and the pre-trained classifiers, such as SVM, AdaBoost, and random forest, are also used for FE recognition based on the extracted features. Conventional approaches require relatively lower computing power and memory than deep learning-based approaches. Therefore, these approaches are still being studied for use in real-time embedded systems because of their low computational complexity and high degree of accuracy [ 22 ]. However, feature extraction and the classifiers should be designed by the programmer and they cannot be jointly optimized to improve performance [ 36 , 37 ]. Table 2 summarizes the representative conventional FER approaches and their main advantages. A summary of publicly available databases related to FER. Stepwise linear discriminant analysis SWLDA used to select the localized features from the expression. In recent decades, there has been a breakthrough in deep-learning algorithms applied to the field of computer vision, including a CNN and recurrent neural network RNN. These deep-learning-based algorithms have been used for feature extraction, classification, and recognition tasks. For these reasons, CNN has achieved state-of-the-art results in various fields, including object recognition, face recognition, scene understanding, and FER. A CNN contains three types of heterogeneous layers: Convolutional layers take image or feature maps as the input, and convolve these inputs with a set of filter banks in a sliding-window manner to output feature maps that represent a spatial arrangement of the facial image. The weights of convolutional filters within a feature map are shared, and the inputs of the feature map layer are locally connected [ 45 ]. Second, subsampling layers lower the spatial resolution of the representation by averaging or max-pooling the given input feature maps to reduce their dimensions and thereby ignore variations in small shifts and geometric distortions [ 45 , 46 ]. The last fully connected layers of a CNN structure compute the class scores on the entire original image. Breuer and Kimmel [ 47 ] employed CNN visualization techniques to understand a model learned using various FER datasets, and demonstrated the capability of networks trained on emotion detection, across both datasets and various FER-related tasks. Jung et al. These two models are combined using a new integration method to boost the performance of facial expression recognition. The complete network is end-to-end trainable, and automatically learns representations robust to variations inherent within a local region. However, because CNN-based methods cannot reflect temporal variations in the facial components, a recent hybrid approach combining a CNN for the spatial features of individual frames, and long short-term memory LSTM for the temporal features of consecutive frames, was developed. LSTMs are explicitly designed to solve the long-term dependency problem using short-term memory. An LSTM has a chain-like structure, although the repeating modules have a different structure, as shown in Figure 4. All recurrent neural networks have a chain-like form of four repeating modules of a neural network [ 50 ]:. The cell state is a horizontal line running through the top of the diagram, as shown in Figure 4. The basic structure of an LSTM, adapted from [ 50 ]. Second, an LSTM supports both fixed-length and variable-length inputs or outputs [ 51 ]. Kahou et al. In this work, the authors presented a complete system for the Emotion Recognition in the Wild EmotiW Challenge [ 52 ], and proved that a hybrid CNN-RNN architecture for a facial expression analysis can outperform a previously applied CNN approach using temporal averaging for aggregation. Kim et al. The spatial image characteristics of the representative expression-state frames are learned using a CNN. In the second part, temporal characteristics of the spatial feature representation in the first part are learned using an LSTM of the facial expression. Chu et al. First, the spatial representations are extracted using a CNN, which is able to reduce person-specific biases caused by handcrafted descriptors e. To model the temporal dependencies, LSTMs are stacked on top of these representations, regardless of the lengths of the input video sequences. Hasani and Mahoor [ 54 ] proposed the 3D Inception-ResNet architecture followed by an LSTM unit that together extracts the spatial relations and temporal relations within the facial images between different frames in a video sequence. Facial landmark points are also used as inputs of this network, emphasizing the importance of facial components rather than facial regions, which may not contribute significantly to generating facial expressions. Graves et al. Jain et al. Initially, this approach subtracts the background and isolates the foreground from the images, and then extracts the texture patterns and the relevant key features of the facial points. The relevant features are then selectively extracted, and an LSTM-CNN is employed to predict the required label for the facial expressions. Commonly, deep learning-based approaches determine features and classifiers by deep neural networks experts, unlike conventional approaches. Deep learning-based approaches extract optimal features with the desired characteristics directly from data using deep convolutional neural networks. However, it is not easy to collect a large amount of training data for the facial emotion under the different conditions enough to learn deep neural networks. Moreover, deep learning-based approaches require more a higher-level and massive computing device than convention approaches to operate training and testing [ 35 ]. Therefore, it is necessary to reduce the computational burden at inference time of deep learning algorithm. Spatial image characteristics of the representative expression-state frames are learned using a CNN. Temporal characteristics of the spatial feature representation in the first part are learned using an LSTM. LSTM unit that together extracts the spatial relations and temporal relations within facial images. Using a recurrent network for temporal dependencies present in the image sequences during classification. Extraction of the texture patterns and the relevant key features of the facial points. Therefore, this hybrid model can learn to recognize and synthesize temporal dynamics for tasks involving sequential images. As shown in Figure 5 , each visual feature determined through a CNN is passed to the corresponding LSTM, and produces a fixed or variable-length vector representation. The outputs are then passed into a recurrent sequence-learning module. Finally, the predicted distribution is computed by applying softmax [ 51 , 53 ]. Overview of the general hybrid deep-learning framework for FER. In the field of FER, numerous databases have been used for comparative and extensive experiments. Traditionally, human facial emotions have been studied using either 2D static images or 2D video sequences. A 2D-based analysis has difficulty handling large pose variations and subtle facial behaviors. The analysis of 3D facial emotions will facilitate an examination of the fine structural changes inherent in spontaneous expressions [ 40 ]. Therefore, this sub-section briefly introduces some popular databases related to FER consisting of 2D and 3D video sequences and still images:. The age range of its subjects is from 18 to 30 years, most of who are female. Image sequences may be analyzed for both action units and prototypic emotions. It provides protocols and baseline results for facial feature tracking, AUs, and emotion recognition. Compound Emotion CE [ 17 ]: CE contains images corresponding to 22 categories of basic and compound emotions for its human subjects females and males, mean age of Most ethnicities and races are included, including Caucasian, Asian, African, and Hispanic. Facial occlusions are minimized, with no glasses or facial hair. Male subjects were asked to shave their faces as cleanly as possible, and all participants were also asked to uncover their forehead to fully show their eyebrows. The database also includes 66 facial landmark points for each image in the database. It was designed for research on 3D human faces and facial expressions, and for the development of a general understanding of human behavior. It contains a total of subjects, 56 females and 44 males, displaying six emotions. There are 25 3D facial emotion models per subject in the database, and a set of 83 manually annotated facial landmarks associated with each model. The JAFFE database contains images of seven facial emotions six basic facial emotions and one neutral posed by ten different female Japanese models. Each image was rated based on six emotional adjectives using 60 Japanese subjects. This database consists of a set of 16, facial images taken under a single light source, and contains 28 distinct subjects for viewing conditions, including nine poses for each of 64 illumination conditions. MMI [ 43 ]: MMI consists of over video sequences and high-resolution still images of 75 subjects. It is fully annotated for the presence of AUs in the video sequences event coding , and partially coded at the frame-level, indicating for each frame whether an AU is in a neutral, onset, apex, or offset phase..

United States: McGraw-Hill, A Contribution to the Ontogenesis of Social Relations. American Sign Language: A teacher's resource text on grammar and culture. Silver Spring, MD: The linguistics of British Sign Language. Friesen; P. Ellsworth Guidelines for research and a review of Category emotion expression face facial. New York: Experimental Psychology.

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Henry Holt. A review of the cross-cultural studies". Emotion Review. Proceedings of the National Academy of Sciences. Friesen Journal of Personality Category emotion expression face facial Social Psychology. Facial Action Coding System 3. Manual of Scientific Codification of the Human Face. Russell Judging emotion from the face in context".

September Journal of Nonverbal Behavior. Darwin and facial expression: Category emotion expression face facial, MA: Malor Books. Yugioh gx alexis naked nude sexy. Facial emotion recognition FER is an important topic in the fields of computer vision and artificial intelligence owing to its significant academic and commercial potential.

This link provides a brief review of researches in the field of FER conducted over the past decades. First, conventional FER approaches are described along with a summary of the representative categories of FER systems and their main algorithms. This review also focuses on an up-to-date hybrid deep-learning approach combining a convolutional neural network CNN for the spatial features of an individual frame and long short-term memory LSTM for temporal features of consecutive frames.

In the later part of this paper, a brief review of publicly available evaluation metrics is given, and a comparison with benchmark results, which are a standard for a quantitative comparison of FER researches, is described.

This review can serve as a brief guidebook to newcomers in the field of FER, providing basic knowledge and a general understanding of the latest state-of-the-art studies, as well as to experienced researchers looking for productive directions for future work.

Facial emotions are important factors in human communication that help us Category emotion expression face facial the intentions of others. In general, people infer the emotional states of other people, such as joy, sadness, and anger, using facial expressions and vocal tone. According to Category emotion expression face facial surveys [ 12 ], verbal components convey one-third of human communication, and nonverbal components convey two-thirds.

Therefore, it is natural that research of facial emotion has been gaining lot of attention over the past decades with Category emotion expression face facial not only in the perceptual and cognitive sciences, but also in affective computing and computer animations [ 2 ].

Interest in automatic facial emotion recognition FER Expanded form of the acronym FER is different in every paper, such as facial emotion recognition and facial expression recognition. In this paper, the term FER refers to facial emotion recognition as this study deals Category emotion expression face facial the general aspects of recognition of facial emotion expression.

This paper first divides researches on automatic FER into two groups according to whether the features are handcrafted or generated through the output of a deep neural network. First, a face image is detected from an input image, and facial components e. Second, various spatial and temporal features are extracted from the facial components. Third, the pre-trained FE classifiers, such as a support vector machine SVMAdaBoost, and random forest, produce the recognition results using the extracted features.

Procedure used Category emotion expression face facial conventional FER approaches: In contrast to traditional approaches using handcrafted features, deep learning has emerged as a general approach to machine learning, yielding state-of-the-art results in many computer vision studies with the availability of big data [ 11 ]. Among the several deep-learning models available, the convolutional neural network CNNa particular type of deep learning, is the most popular network model.

In CNN-based approaches, the input image is convolved through a filter collection in the convolution layers to produce a feature Category emotion expression face facial. Each feature map is then combined to fully connected networks, and the face expression is recognized as belonging to a particular class-based the output of the softmax algorithm.

FER can also be divided into two groups according to whether it uses frame or video images [ 13 ]. First, static frame-based FER relies solely on static facial features obtained by extracting handcrafted features from selected peak expression frames of image sequences.

Second, dynamic video-based FER utilizes spatio-temporal features to capture the expression dynamics in facial expression sequences.

For example, the extracted dynamic features have different transition durations and different feature characteristics of the facial expression depending on the particular faces. Before reviewing researches related to FER, special terminology Category emotion expression face facial an important role in FER research is listed below:. The facial action coding system FACS is a system based on facial muscle changes and can characterize facial actions to express individual human emotions as defined by Ekman and Friesen [ 14 ] in Source encodes the movements of specific facial muscles called action units AUswhich reflect distinct momentary changes in facial appearance [ 15 ].

Facial landmarks FLs are visually salient points in facial regions such as the end of the nose, ends of the eye brows, and the mouth, as described in Figure 1 b.

The pairwise positions of each of two landmark points, or the local texture of a landmark, are used as a feature vector of FER. In general, FL detection approaches can be categorized into Category emotion expression face facial types according to the generation of models such as active shape-based model ASM and appearance-based model AAMcheck this out regression-based model with a combination of local and global models, and CNN-based methods.

FL models are trained model from the appearance and shape variations from a coarse initialization. Then, the initial shape is moved to a better position step-by-step until convergence [ 16 ]. Basic emotions BEs are seven basic human emotions: Sample examples of various facial emotions and AUs: Compound emotions CEs are a combination of two basic emotions.

Du et al.

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Figure 3 b shows some examples of CE. Micro expressions MEs indicate more spontaneous and Category emotion expression face facial facial movements that occur involuntarily. Figure 3 c shows some examples of MEs. Facial action units AUs code the fundamental actions 46 AUs of individual or groups of muscles typically seen when producing the facial expressions of a particular emotion [ 17 ], as shown in Figure 3 d.

To recognize facial emotions, Category emotion expression face facial AU is detected and the system classify facial category according to the combination of AUs. Prototypical AUs observed in each basic and compound emotion category, adapted from [ 18 ]. Table 1 shows the prototypical AUs observed in each basic and compound emotion category. Some review papers [ 1920 ] have focused solely on conventional researches without introducing deep-leaning-based approaches. Recently, Ghayoumi [ 21 ] introduced a quick review of deep learning in FER.

However, only a review of simple differences between conventional approaches and deep-learning-based approaches was provided. The main contributions of this review are as follows:. The focus is on providing a general understanding of the state-of-the art FER approaches, and helping new researchers understand the essential components and trends in the FER field. Various standard databases that include still images and video sequences for FER use are introduced, along with their purposes and characteristics.

Key aspects are compared between conventional FER and Category emotion expression face facial FER in terms of accuracy Category emotion expression face facial resource requirements. Therefore, many current FER algorithms are still being used Category emotion expression face facial embedded systems, including smartphones. The remainder of this paper is organized as follows. In Section 2conventional FER approaches are described along with a summary of the representative categories of FER systems and their main algorithms.

In Section 3advanced FER approaches using deep-learning algorithms are presented. In Section 4 and Section 5a brief review of publicly available FER database and evaluation metrics with a comparison with benchmark results are provided.

Finally, Section 6 offers some concluding remarks and discussion of future work. For automatic FER systems, various types of conventional approaches have been studied. The commonality of these approaches is detecting the face region and extracting geometric features, appearance features, or a hybrid of geometric and appearance features on the target face. For the geometric features, the relationship between facial components is used to construct a feature vector Category emotion expression face facial training [ https://closeup.printablehd.host/post1340-mone.php23 ].

Ghimire and Lee [ 23 ] used two types of geometric features based on the position and angle of 52 facial landmark points. First, the angle and Euclidean distance between each pair of landmarks within a frame Category emotion expression face facial calculated, and second, the distance and angles are subtracted from the corresponding distance and angles in the first frame of the video sequence.

Sex natasha Watch Porn Videos Pussy Bukkake. Combining the two approaches to detection defined in this section should yield even more accurate results in low-resolution images and under occlusions or other image manipulations. We hope that more research will be devoted to this important topic in face recognition. The approaches defined in this section are a good start, but much research is needed to make these systems comparable to human accuracies. We argue that research in machine learning should address these problems rather than the typical classification one. A first goal is to define algorithms that can detect face landmarks very accurately even at low resolutions. Kernel methods and regression approaches are surely good solutions as illustrated above. But more targeted approaches are needed to define truly successful computational models of the perception of facial expressions of emotion. In the real world, occlusions and unavoidable imprecise detections of the fiducial points, among others, are known to affect recognition Torre and Cohn, ; Martinez, Additionally, some expressions are, by definition, ambiguous. Most importantly though seems to be the fact that people are not very good at recognizing facial expressions of emotion even under favorable condition Du and Martinez, Humans are very robust at detection joy and surprise from images of faces; regardless of the image conditions or resolution. However, we are not as good at recognizing anger and sadness and are worst at fear and disgust. The above results suggest that there could be three groups of expressions of emotion. The first group is intended for conveying emotions to observers. These expressions have evolved a facial construct i. Example expressions in this group are happiness and surprise. A computer vision system—especially a HCI—should make sure these expressions are accurately and robustly recognized across image degradation. Therefore, we believe that work needs to be dedicated to make systems very robust when recognizing these emotions. The second group of expressions e. A computer vision system should recognize these expressions in good quality images, but can be expected to fail as the image degrades due to resolution or other image manipulations. An interesting open question is to determine why this is the case and what can be learned about human cognition from such a result. The third and final group of emotions constitutes those at which humans are not very good recognizers. This includes expressions such as fear and disgust. Early work especially in evolutionary psychology had assumed that recognition of fear was primal because it served as a necessary survival mechanism LeDoux, Recent studies have demonstrated much the contrary. Fear is generally poorly recognized by healthy human subjects Smith and Schyns, ; Du and Martinez, One hypothesis is that expressions in this group have evolved for other than communication reasons. For example, it has been proposed that fear opens sensory channels i. Note that people can be trained to detect such changes quite reliably Ekman and Rosenberg, , but this is not the case for the general population. Another area that will require additional research is to exploit other types of facial expressions. Facial expressions are regularly used by people in a variety of setting. More research is needed to understand these. Moreover, it will be important to test the model in natural occurring environments. Collection and handling of this data poses several challenges, but the research described in these pages serves as a good starting point for such studies. In such cases, it may be necessary to go beyond a linear combination of basic categories. However, without empirical proof for the need of something more complex than linear combinations of basic emotion categories, such extensions are unlikely. The cognitive system has generally evolved the simplest possible algorithms for the analysis or processing of data. Strong evidence of more complex models would need to be collected to justify such extensions. One way to do this is by finding examples that cannot be parsed by the current model, suggesting a more complex structure is needed. It is important to note that these results will have many applications in studies of agnosias and disorders. Of particular interest are studies of depression or anxiety disorders. Depression afflicts a large number of people in the developed countries. Models that can help us better understand its cognitive processes, behaviors and patterns could be of great importance for the design of coping mechanisms. Improvements may also be possible if it were to better understand how facial expressions of emotion affect these people. Other syndromes such as autism are also of great importance these days. More children than ever are being diagnosed with the disorder CDC, ; Prior, We know that autistic children do not perceive facial expressions of emotion as others do Jemel et al. A modified computational model of the perception of facial expressions of emotion in autism could help design better teaching tools for this group and may bring us closer to understanding the syndrome. There are indeed many great possibilities for machine learning researchers to help move these studies forward. Extending or modifying the modeled summarized in the present paper is one way. Developing machine learning algorithms to detect face landmark more accurately is another. Developing statistical tools that more accurately represent the underlying manifold or distribution of the data is yet another great way to move the state of the art forward. In the present work we have summarized the development of a model of the perception of facial expressions of emotion by humans. A key idea in this model is to linearly combine a set of face spaces defining some basic emotion categories. The model is consistent with our current understanding of human perception and can be successfully exploited to achieve great recognition results for computer vision and HCI applications. We have shown how, to be consistent with the literature, the dimensions of these computational spaces need to encode configural and shape features. We conclude that to move the state of the art forward, face recognition research has to focus on a topic that has received little attention in recent years—precise, detailed detection of faces and facial features. Although we have focused our study on the recognition of facial expressions of emotion, we believe that the results apply to most face recognition tasks. We have listed a variety of ways in which the machine learning community can get involved in this research project and briefly discussed applications in the study of human perception and the better understanding of disorders. J Mach Learn Res. Author manuscript; available in PMC Aug Aleix Martinez and Shichuan Du. Aleix Martinez: Copyright notice. The publisher's final edited version of this article is available at J Mach Learn Res. See other articles in PMC that cite the published article. Abstract In cognitive science and neuroscience, there have been two leading models describing how humans perceive and classify facial expressions of emotion—the continuous and the categorical model. Introduction The face is an object of major importance in our daily lives. Facial Expressions: From Production to Perception The human face is an engineering marvel. Open in a separate window. Figure 1. A Model of the Perception of Facial Expressions of Emotion In cognitive science and neuroscience researchers have been mostly concerned with models of the perception and classification of the six facial expressions of emotion listed above. Figure 2. Figure 3. Dimensions of the Model In the early years of computer vision, researchers derived several feature- and shape-based algorithms for the recognition of objects and faces Kanade, ; Marr, ; Lowe, Figure 4. Figure 5. Figure 6. Precise Detection of Faces and Facial Features As seen thus far, human perception is extremely tuned to small configural and shape changes. Figure 7. Two example of imprecise detections of a face with a state of the art algorithm. Figure 8. Figure 9. Figure Discussion In the real world, occlusions and unavoidable imprecise detections of the fiducial points, among others, are known to affect recognition Torre and Cohn, ; Martinez, Conclusions In the present work we have summarized the development of a model of the perception of facial expressions of emotion by humans. Inversion and configuration of faces. Cognitive Psychology. Categorical effects in the perception of faces. Face recognition: Computer-enhanced emotion in facial expressions. Proceedings of the Royal Society of London B. Neuropsychology of fear and loathing. Nature Review Neuroscience. Understanding emotions from standardized facial expressions in autism and normal development. Center for Disease Control and Prevention. Prevalence of autism spectrum disorders autism and developmental disabilities monitoring network, 14 sites, united states, Active appearance models. Emotion, Reason, and the Human Brain. The Expression of the Emotions in Man and Animal. Murray; London: Features versus context: An approach for precise and detailed detection and delineation of faces and facial features. The resolution of facial expressions of emotion. Journal of Vision. Pictures of Facial Affect. What the Face Reveals: Oxford University Press; New York: Computing smooth time-trajectories for camera and deformable shape in structure from motion with occlusion. Kernel non-rigid structure from motion. Bayes optimality in linear discriminant analysis. Rotation invariant kernels and their application to shape analysis. Active appearance models with rotation invariant kernels. IEEE Proc. International Conference on Computer Vision; b. Bartneck C. HCI and the face: Interaction Design and Usability; Beijing, China. Hickson S. Classifying facial expressions in VR using eye-tracking cameras. Chen C. Augmented reality-based self-facial modeling to promote the emotional expression and social skills of adolescents with autism spectrum disorders. Assari M. Zhan C. A real-time facial expression recognition system for online games. Games Technol. Competitive affective gaming: Lucey P. Kahou S. Walecki R. Deep structured learning for facial expression intensity estimation. Image Vis. Kim D. Multi-objective based Spatio-temporal feature representation learning robust to expression intensity variations for facial expression recognition. IEEE Trans. Ekman P. Facial Action Coding System: Hamm J. Automated facial action coding system for dynamic analysis of facial expressions in neuropsychiatric disorders. Jeong M. Driver facial landmark detection in real driving situation. Circuits Syst. Video Technol. Tao S. Compound facial expressions of emotion. Benitez-Quiroz C. Kolakowaska A. A review of emotion recognition methods based on keystroke dynamics and mouse movements; Proceedings of the 6th International Conference on Human System Interaction; Gdansk, Poland. Kumar S. Facial expression recognition: Ghayoumi M. A quick review of deep learning in facial expression. Suk M. Ghimire D. Geometric feature-based facial expression recognition in image sequences using multi-class AdaBoost and support vector machines. Happy S. A real time facial expression classification system using local binary patterns; Proceedings of the 4th International Conference on Intelligent Human Computer Interaction; Kharagpur, India. Siddiqi M. Human facial expression recognition using stepwise linear discriminant analysis and hidden conditional random fields. Image Proc. Khan R. Framework for reliable, real-time facial expression recognition for low resolution images. Pattern Recognit. Facial expression recognition based on local region specific features and support vector machines. Tools Appl. Torre F. Polikovsky S. Sandbach G. Static and dynamic 3D facial expression recognition: A comprehensive survey. Zhao G. Facial expression recognition from near-infrared videos. Shen P. Facial expression recognition from infrared thermal videos. Szwoch M. Gunawan A. Face expression detection on Kinect using active appearance model and fuzzy logic. Procedia Comput. Wei W. Tian Y. Deshmukh S. Survey on real-time facial expression recognition techniques. IET Biom. Mavadati S. A spontaneous facial action intensity database. Maalej A. Shape analysis of local facial patches for 3D facial expression recognition. Yin L. Lyons M. LeCun Y. Backpropagation applied to handwritten zip code recognition. Neural Comput. Genetic algorithm based filter bank design for light convolutional neural network. Breuer R. A deep learning perspective on the origin of facial expressions. Jung H. Zhao K. Olah C. Donahue J. Long-term recurrent convolutional networks for visual recognition and description. Pattern Anal. Chu W. Hasani B. Graves A. Facial expression recognition with recurrent neural networks; Proceedings of the International Workshop on Cognition for Technical Systems; Santorini, Greece. Jain D. Multi angle optimal pattern-based deep learning for automatic facial expression recognition. Yan W. An improved spontaneous micro-expression database and the baseline evaluation. Zhang X. A high resolution spontaneous 3D dynamic facial expression database. Kohavi R. Ding X. Huang M. Zhen W. Zhang S. Robust facial expression recognition via compressive sensing. Dynamic texture recognition using local binary patterns with an application to facial expressions. Jiang B. Lee S. Collaborative expression representation using peak expression and intra class variation face images for practical subject-independent emotion recognition in videos. Liu M. Deeply learning deformable facial action parts model for dynamic expression analysis; Proceedings of the Asian Conference on Computer Vision; Singapore. AU-inspired deep networks for facial expression feature learning. Mollahosseini A. Recent advances in convolutional neural networks. Support Center Support Center. External link. Please review our privacy policy. Sadly surprised. Additionally, coefficients of internal consistency and factor saturation were presented for each task—including emotion-specific results when possible. Taken together, the 16 tasks worked well: They were neither too easy nor too hard for the participants, and internal consistency and factor saturation were satisfactory. With respect to mean performance across all emotion domains and tasks there was an advantage for happy faces in comparison to all other facial expressions. This finding parallels several previous reports of within- and across-subject studies on facial expression recognition e. With respect to results concerning the covariance structure it might be argued that some of the emotion-specific results are not promising enough because some of the psychometric results are still in the lower range of desirable magnitudes. However, the tasks presented here ought not to be considered as stand-alone measures. Instead, preferably a compilation of these tasks should be jointly used to measure important facets of emotion-related interpersonal abilities. Methodologically, the tasks presented here would thus serve like the items of a conventional test as indicators below presupposed latent factors. Additionally, some of the unsatisfactory psychometric coefficients are likely to improve if test length is increased. Depending on available resources in a given study or application context and in line with the measurement intention tasks for one or more ability domains can be sampled from the present collection. We recommend sampling three or more tasks per ability domain. The duration estimates provided in Table 1 facilitate compilation of such task batteries in line with pragmatic needs of a given study or application context. In this paper, we presented a variety of tasks for the purpose of capturing individual differences in emotion perception and emotion recognition. The strategy in developing the present set of tasks was to sample measures established in experimental psychology and to adapt them for psychometric purposes. It is important to note that the predominant conceptual model in individual differences psychology presupposes effect indicators of common constructs. In these models, individual differences in indicators are caused by individual differences in at least one latent variable. Specific indicators in such models can be conceived as being sampled from a domain or range of tasks. Research relying on single indicators sample just one task from this domain and are therefore analogous to single case studies sampling just a single person. The virtue of sampling more than a single task is that further analysis of a variety of such measures allows abstracting not only from measurement error but also from task specificity. In elaboration of this sampling concept we defined the domain from which we were sampling tasks a priori. Although general principles of sampling tasks from a domain have been specified, implicitly by Campbell and Fiske and Cattell and more explicitly by Little et al. In the present context, we applied a first distinction, which is well established in research on individual differences in cognitive abilities, namely between speed and accuracy tasks. A second distinction is based on the cognitive demand perception vs. A speed task is defined as being so simple that members of the application population complete all tasks correctly if given unlimited time. An accuracy task is defined as being so hard that a substantial proportion of the application population cannot complete it correctly even if given unlimited time. We expect that once the guidelines and criteria suggested for tasks in the introduction are met and the following classifications of demands are applied— a primarily assessing emotion perception or emotion recognition and b provoking behavior that can be analyzed by focusing on either speed or accuracy, no further substantial determinants of individual differences can be established. Therefore, we anticipate that the expected diversity Little et al. This statement might seem very bold but we need to derive and test such general statements in order to avoid mixing up highly specific indicators with very general constructs. Obviously, this statement about task sampling applies to measures already developed some of which were discussed in the introduction and to measures still to be developed. A broad selection of tasks can be seen as a prerequisite to firmly establish the structure of individual differences in a domain under investigation. This is vividly visible in review work on cognitive abilities Carroll, It is not yet sufficiently clear how the arguments concerning task sampling apply to prior work on individual differences in socio-emotional abilities. Contemporary theories of socio-emotional abilities e. For example, the currently most prominent theory of emotional intelligence—the four-branch model by Mayer et al. It is important to note that many of the prevalent measures of emotional intelligence, which rely on self-report of typical behavior, do not meet standards that should be applied to cognitive ability measures Wilhelm, Assessment tools of emotional intelligence, which utilize maximum effort performance in ability measures, meet some but not all of these criteria Davies et al. Arguably, these standards are met by the tasks discussed in the theoretical section and newly presented here. A convergent validation of the tasks presented here with popular measures of emotional intelligence—such as emotion perception measures in the MSCEIT Mayer et al. This paper presents a compilation of assessment tools. Their purpose is to allow a psychologically based and psychometrically sound measurement of highly important receptive socio-emotional abilities, that is, the perception and recognition of facial expressions of emotions. We think that the present compilation has a variety of advantages over available measures and we stressed these advantages at several places in this paper. The goal was to provide a sufficiently detailed description of the experimental procedures for each task, to provide difficulty and reliability estimates for tasks and emotion specific subscales created within tasks. In further research we will consider the factorial structure across tasks and investigate competing measurement models of emotion perception and recognition—applying theoretically important distinctions between speed vs. An essential and indispensable step for such further research is the close inspection of the psychometric quality of each task. The application populations of the present tasks are older adolescents and adults and task difficulty was shown to be somewhere between adequate and optimal for younger adults included in the present sample. With some adaptations the tasks can be applied to other populations too. The goal of our research is met best, when these tools and adaptations or variants of them are frequently used in many different research fields. We will briefly present some research directions we currently pursue in order to illustrate potential uses of our battery. One question we are currently investigating is the distinction between the perception and recognition of unfamiliar neutral faces Herzmann et al. Investigating such questions with psychometric methods is important in order to provide evidence that facial emotion reception is a determinant of specific individual differences. In elaboration of this research branch we also study individual differences in posing emotional expressions Olderbak et al. Obviously, the measures presented in this paper are also a promising approach when studying group differences—for example when studying differences between psychopathic and unimpaired participants Marsh and Blair, Establishing deficient socio-emotional interpersonal abilities as key components of mental disorders hinges upon a solid measurement in many cases. We hope that the present contribution helps to provide such measurements. Finally, we want to mention two important restrictions of the face stimuli used in the present tasks. First, the stimuli are exclusively portraits of white young adult middle-Europeans. It would therefore be adequate to create additional stimuli showing subjects of other ethnicity. Walla and Panksepp, Obviously, the tasks presented here could be used with stimulus sets varying in origin, ethnicity, color, age etc. Software code and more detail concerning the experimental setup and task design are available from the authors upon request. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Adams, R. Perceived gaze direction and the processing of facial displays of emotion. Allison, P. Missing Data. Thousand Oaks, CA: Sage Publications. Ambadar, Z. Deciphering the enigmatic face. The importance of facial dynamics in interpreting subtle facial expressions. Emotion recognition from expressions in face, voice, and body: Emotion 9, — Barnett, V. Outliers in Statistical Data. New York, NY: Baron-Cohen, S. Child Psychol. Psychiatry 42, — CrossRef Full Text. Bassili, J. Emotion recognition: Bommer, W. Nonverbal emotion recognition and performance: Managerial Psychol. Bowers, D. The Florida affect battery, Revised. Gainsville, FL: Bruce, V. Understanding face recognition. Calder, A. Calder, G. Rhodes, M. Johnson, and J. Haxby Oxford: Oxford University Press , — Understanding the recognition of facial identity and facial expression. Categorical perception of morphed facial expressions. Calvo, M. Detection of emotional faces: Campbell, D. Covergent and discriminant validation by the multitrait-multimethod matrix. Carroll, J. Human Cognitive Abilities: A Survey of Factor-analytic Studies. Cambridge University Press. Cattell, R. Theory of situational, instrument, second order, and refraction factors in personality structure research. Pubmed Abstract Pubmed Full Text. Cohen, J. Temporal dynamics of brain activation during a working memory task. Nature , — D'Argembeau, A. Identity but not expression memory for unfamiliar faces is affected by ageing. Memory 12, — Davies, G. Similarity effects in face recognition. Davies, M. Emotional intelligence: Den Uyl, M. Noldus, F. Grieco, L. Loijens, and P. Zimmerman The Netherlands: Ebner, N. FACES—a database of facial expressions in young, middle-aged, and older women and men: Methods 42, — Ekman, P. Detecting deception from the body or face. Personality Soc. Pictures of Facial Affect. Palo Alto, CA: Consulting Psychologists Press. Emotion and the Human Face: Guidelines for Research and an Integration of Findings. Pergamon Press. Elfenbein, H. Predicting workplace outcomes from the ability to eavesdrop on feelings. On the universality and cultural specificity of emotion recognition: Is there an in-group advantage in emotion. Embretson, S. Construct validity: Etcoff, N. Categorical perception of facial expressions. Cognition 44, — Frearson, W. Intelligence, reaction-time RT and a new odd-man-out RT paradigm. Frischen, A. Visual search for faces with emotional expressions. Froming, K. The Comprehensive Affect Testing System. Psychology Software, Inc. Available online at: Fujimura, T. Categorical and dimensional perceptions in decoding emotional facial expressions. Grady, C. The effect of age on memory for emotional faces. Neuropsychology 21, — Haxby, J. Oxford University Press , 93— Hess, U. The intensity of emotional facial expressions and decoding accuracy. Nonverbal Behav. Herzmann, G. Toward a comprehensive test battery for face cognition: Methods 40, — Hildebrandt, A. Measuring the speed of recognizing facially expressed emotions. Hoffmann, H. Expression intensity, gender and facial emotion recognition: Acta Psychol. Hoheisel, B. Izard, C. The Face of Emotion. Jack, R. Cultural confusions show that facial expressions are not universal. Judd, C. Treating stimuli as a random factor in social psychology: Kamachi, M. Dynamic properties influence the perception of facial expressions. Perception 30, — Kessler, H. Facially expressed emotion labeling FEEL: Verhaltenstherapie und Verhaltensmedizin 23, — Die Messung von Emotionserkennung mittels computer-morphing. Nervenheilkunde 24, — Lang, P. University of Florida. Lawrence, M. Easy Analysis and Visualization of Factorial Experiments. R Package Version 3. Little, T. On selecting indicators for multivariate measurement and modeling with latent variables. Methods 4, — MacCann, C. New paradigms for assessing emotional intelligence: Emotion 8, — Marsh, A. Deficits in facial affect recognition among antisocial populations: Matsumoto, D. Judgments of facial expressions of emotion in profile. Emotion 11, — A new test to measure emotion recognition ability: Mayer, J. Human abilities: Technical Manual. Toronto, ON: Multi-Health Systems. McDonald, R. Test Theory: A Unified Treatment. Mahwah, NJ: McKelvie, S. Emotional expression in upside-down faces: Meissner, C. A facial expression [1] is one or more motions or positions of the muscles beneath the skin of the face. According to one set of controversial theories, these movements convey the emotional state of an individual to observers. Facial expressions are a form of nonverbal communication. Humans can adopt a facial expression voluntarily or involuntarily, and the neural mechanisms responsible for controlling the expression differ in each case. Voluntary facial expressions are often socially conditioned and follow a cortical route in the brain. Conversely, involuntary facial expressions are believed to be innate and follow a subcortical route in the brain. Facial recognition is often an emotional experience for the brain and the amygdala is highly involved in the recognition process. The eyes are often viewed as important features of facial expressions. Aspects such as blinking rate can possibly be used to indicate whether a person is nervous or whether he or she is lying. Also, eye contact is considered an important aspect of interpersonal communication. However, there are cultural differences regarding the social propriety of maintaining eye contact or not. Beyond the accessory nature of facial expressions in spoken communication between people, they play a significant role in communication with sign language. Many phrases in sign language include facial expressions in the display. There is controversy surrounding the question of whether facial expressions are worldwide and universal displays among humans. Supporters of the Universality Hypothesis claim that many facial expressions are innate and have roots in evolutionary ancestors. Opponents of this view question the accuracy of the studies used to test this claim and instead believe that facial expressions are conditioned and that people view and understand facial expressions in large part from the social situations around them. Moreover, facial expressions have a strong connection with personal psychology. Some psychologists have the ability to discern hidden meaning from person's facial expression. One experiment investigated the influence of gaze direction and facial expression on face memory. Participants were shown a set of unfamiliar faces with either happy or angry facial expressions, which were either gazing straight ahead or had their gaze averted to one side. Memory for faces that were initially shown with angry expressions was found to be poorer when these faces had averted as opposed to direct gaze, whereas memory for individuals shown with happy faces was unaffected by gaze direction. It is suggested that memory for another individual's face partly depends on an evaluation of the behavioural intention of that individual. Facial expressions are vital to social communication between humans. They are caused by the movement of muscles that connect to the skin and fascia in the face. These muscles move the skin, creating lines and folds and causing the movement of facial features, such as the mouth and eyebrows. These muscles develop from the second pharyngeal arch in the embryo. The temporalis , masseter , and internal and external pterygoid muscles , which are mainly used for chewing, have a minor effect on expression as well. These muscles develop from the first pharyngeal arch. There are two brain pathways associated with facial expression; the first is voluntary expression. Voluntary expression travels from the primary motor cortex through the pyramidal tract , specifically the corticobulbar projections. The cortex is associated with display rules in emotion, which are social precepts that influence and modify expressions. Cortically related expressions are made consciously. The second type of expression is emotional. These expressions originate from the extrapyramidal motor system , which involves subcortical nuclei. For this reason, genuine emotions are not associated with the cortex and are often displayed unconsciously. This is demonstrated in infants before the age of two; they display distress, disgust, interest, anger, contempt, surprise, and fear. Infants' displays of these emotions indicate that they are not cortically related. Similarly, blind children also display emotions, proving that they are subconscious rather than learned. Other subcortical facial expressions include the "knit brow" during concentration, raised eyebrows when listening attentively, and short "punctuation" expressions to add emphasis during speech. People can be unaware that they are producing these expressions. The amygdala plays an important role in facial recognition..

For the classifier, two methods are presented, either using multi-class AdaBoost with dynamic time warping, or using a SVM on the boosted feature vectors. As an example of using global features, Happy et al. Although this method is implemented in real time, the recognition accuracy tends to be degraded because it cannot reflect local variations of the facial components to Category emotion expression face facial feature vector.

Unlike a global-feature-based approach, different face regions have different levels of importance. Ghimire et al. Important local article source are determined using an incremental search approach, which results in a reduction of the feature dimensions and an improvement in the recognition accuracy. For hybrid features, some approaches [ 1827 ] have combined geometric and appearance features to complement the weaknesses of the two approaches and provide even better results in certain cases.

In video sequences, many systems [ 18222328 ] are used to measure the geometrical displacement of facial landmarks between the current frame and previous frame as temporal features, and extracts appearance features for the spatial features.

The main difference between FER for still images and for video sequences is that the landmarks in the latter are tracked frame-by-frame and the system generates new dynamic features through displacement between the previous and current frames. Similar classification algorithms are then used in the video sequences, as described in Figure 1. To recognize micro-expression, high speed camera is used to capture video sequences of the face.

Polikovsky et al. This study divides face regions into specific regions, and then 3D-Gradients orientation histogram is generated from the motion in each region for FER. Apart from FER of 2D images, 3D and 4D dynamic 3D recordings are increasingly used in expression analysis research because of the problems presented in 2D images caused by inherent variations in pose and illumination.

One Category emotion expression face facial to note in 3D is that dynamic and static system are very different because of the Category emotion expression face facial of data. Static systems extract feature from statistical models such as deformable model, active shape model, analysis of 2D representations, and distance-based features. In contrast, dynamic link utilize 3D image sequences for analysis of facial expressions such as 3D motion-based features.

For FER, 3D images also use the similar conventional classification algorithms [ 2930 ]. Some researchers [ 3132333435 ] have tried to recognize facial emotions using infrared images instead of visible light spectrum VIS image Category emotion expression face facial visible Category emotion expression face facial VIS image is variable according to the status of illumination.

Zhao et al. Shen et al. This study uses local movements within the face area as the feature and recognized facial expressions using relations between particular emotions. To role of AAM is to adjust shape and texture model in a new face, when there is variation of shape and texture comparing to the training result. Wei et al. This study extracts facial feature points vector by face tracking algorithm using captured sensor data and recognize six facial emotions by random forest algorithm. Commonly, conventional approaches determine features and classifiers by experts.

For feature extraction, many Category emotion expression face facial handcrafted feature, such as HoG, LBP, distance and angle relation between landmarks are used and the pre-trained classifiers, such as SVM, AdaBoost, Category emotion expression face facial random forest, are also used Category emotion expression face facial FE recognition based on the extracted features. Conventional approaches require relatively lower article source power and memory than deep learning-based approaches.

Therefore, these approaches are still being studied for use in real-time embedded systems because of their low computational complexity and high degree of accuracy [ 22 ]. However, feature extraction and the classifiers should be designed by the programmer and they cannot be jointly optimized to improve performance [ 3637 ].

Table 2 summarizes the representative conventional FER approaches and their main advantages. A summary of Category emotion expression face facial available databases related to FER.

Stepwise linear discriminant analysis Category emotion expression face facial used to select the localized features from the expression. In recent decades, there has been a breakthrough in deep-learning algorithms applied to the field of computer vision, including a CNN and recurrent neural network RNN. These deep-learning-based algorithms have been used for feature extraction, classification, and recognition tasks. For these reasons, CNN has achieved state-of-the-art results in various fields, including object recognition, face recognition, scene understanding, and FER.

A CNN contains three types of heterogeneous layers: Milf gotta juicebox!!.

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