Abstract—Face images are difficult to interpret because they are highly variable. Sources of variability include individual appearance, 3D pose, facial expression, and lighting. We describe a compact parametrized model of facial appearance which takes into account all these sources of variability. The model represents both shape and gray-level appearance, and is created by performing a statistical analysis over a training set of face images. A robust multiresolution search algorithm is used to fit the model to faces in new images. This allows the main facial features to be located, and a set of shape, and gray-level appearance parameters to be recovered. A good approximation to a given face can be reconstructed using less than 100 of these parameters. This representation can be used for tasks such as image coding, person identification, 3D pose recovery, gender recognition, and expression recognition. Experimental results are presented for a database of 690 face images obtained under widely varying conditions of 3D pose, lighting, and facial expression. The system performs well on all the tasks listed above.

Over the last decade, automatic facial expression analysis has become an active research area that finds potential applications in areas such as more engaging human-computer interfaces, talking heads, image retrieval and human emotion analysis. Facial expressions reflect not only emotions, but other mental activities, social interaction and physiological signals. In this survey we introduce the most prominent automatic facial expression analysis methods and systems presented in the literature. Facial motion and deformation extraction approaches as well as classification methods are discussed with respect to issues such as face normalization, facial expression dynamics and facial expression intensity, but also with regard to their robustness towards environmental changes.

Face images are difficult to interpret because they are highly variable. Sources of variability include individual appear# ance, 3D pose, facial expression and lighting. We describe a compact parametrised model of facial appearance which takes into account all these sources of variability. The model represents both shape and grey-level appearance and is created by performing a statistical analysis over a training set of face images. A robust multi-resolution search algo# rithm is used to fit the model to faces in new images. This allows the main facial features to be located and a set of shape and grey-level appearance parameters to be recov# ered. A good approximation to a given face can be recon# structed using less than 100 of these parameters. This repre# sentation can be used for tasks such as image coding, person identification, pose recovery, gender recognition and ex# pression recognition. The system performs well on all the tasks listed above. 1: Introduction Á ÂÄÀÅÃÇÂÉÀÊÅËÂÈ...

Facial expressions provide an important behavioral measure for the study of emotion, cognitive processes, and social interaction. The Facial Action Coding System ~Ekman & Friesen, 1978! is an objective method for quantifying facial movement in terms of component actions. We applied computer image analysis to the problem of automatically detecting facial actions in sequences of images. Three approaches were compared: holistic spatial analysis, explicit measurement of features such as wrinkles, and estimation of motion flow fields. The three methods were combined in a hybrid system that classified six upper facial actions with 91 % accuracy. The hybrid system outperformed human nonexperts on this task and performed as well as highly trained experts. An automated system would make facial expression measurement more widely accessible as a research tool in behavioral science and investigations of the neural substrates of emotion.

There is strong evidence that face processing in the brain is localized. The double dissociation between prosopagnosia, a face recognition deficit occurring after brain damage, and visual object agnosia, difficulty recognizing other kinds of complex objects, indicates that face and non-face object recognition may be served by partially independent neural mechanisms. In this paper, we use computational models to show how the face processing specialization apparently underlying prosopagnosia and visual object agnosia could be attributed to (1) a relatively simple competitive selection mechanism that, during development, devotes neural resources to the tasks they are best at performing, (2) the developing infant's need to perform subordinate classification (identification) of faces early on, and (3) the infant's low visual acuity at birth. Inspired by de Schonen, Mancini and Liegeois' arguments (1998) [de Schonen, S., Mancini, J., Liegeois, F. (1998). About functional cortical specializat...

Signature Professor Ching-Chung Li Signature Professor Takeo Kanade Signature Professor Jeffrey F. Cohn AUTOMATIC RECOGNITION OF FACIAL EXPRESSIONS USING HIDDEN MARKOV MODELS AND ESTIMATION OF EXPRSSION INTENSITY Jenn-Jier James Lien, Ph.D. Facial expressions provide sensitive cues about emotional responses and play a major role in the study of psychological phenomena and the development of nonverbal communication. Facial expressions regulate social behavior, signal communicative intent, and are related to speech production. Most facial expression recognition systems focus on v only six basic expressions. In everyday life, however, these six basic expressions occur relatively infrequently, and emotion or intent is more often communicated by subtle changes in one or two discrete features, such as tightening of the lips which may communicate anger. Humans are capable of producing thousands of expressions that vary in complexity, intensity, and meaning. The objective of this dissertati...

& There are two competing theories of facial expression recognition. Some researchers have suggested that it is an example of ‘‘categorical perception.’ ’ In this view, expression categories are considered to be discrete entities with sharp boundaries, and discrimination of nearby pairs of expressive faces is enhanced near those boundaries. Other researchers, however, suggest that facial expression perception is more graded and that facial expressions are best thought of as points in a continuous, low-dimensional space, where, for instance, ‘‘surprise’ ’ expressions lie between ‘‘happiness’ ’ and ‘‘fear’’ expressions due to their perceptual similarity. In this article, we show that a simple yet biologically plausible neural network model, trained to classify facial expressions into six basic emotions, predicts data used to support both of these theories. Without any parameter tuning, the model matches a variety of psychological data on categorization, similarity, reaction times, discrimination, and recognition difficulty, both qualitatively and quantitatively. We thus explain many of the seemingly complex psychological phenomena related to facial expression perception as natural consequences of the tasks’ implementations in the brain. &

We present an automated emotion recognition system that is capable of identifying six basic emotions (happy, surprise, sad, angry, fear, disgust) in novel face images. An ensemble of simple feed-forward neural networks are used to rate each of the images. The outputs of these networks are then combined to generate a score for each emotion. The networks were trained on a database of face images that human subjects consistently rated as portraying a single emotion. Such a system achieves 86% generalization on novel face images (individuals the networks were not trained on) drawn from the same database. The neural network model exhibits categorical perception between some emotion pairs. A linear sequenceof morph images is created between two expressions of an individual's face and this sequence is analyzedby the model. Sharp transitions in the output response vector occur in a single step in the sequence for some emotion pairs and not for others. We plan to us the model's response to limi...

We use combinations of feedforward networks trained to recognize emotions in face images to achieve excellent generalization. Networks trained with an input encoding of face features (eyes and mouth) achieved about an 84% generalization rate on novel faces. A similar encoding technique applied to the entire face with the same number of parameters achieved only a 60% generalization rate. This suggests that the actual representational scheme used by the brain to identify emotions may consist of face features rather than the entire face. 1 Introduction In an extension of Cottrell and Metcalfe's work on recognizing emotions in face images [5], the performance of artificial neural networks in classification of emotions in face images is explored. In their work, undergraduates were asked to exhibit a number of different emotions. The images were then compressed with an auto-associative network, and the hidden unit activations for each image were then used as input to another network. The ou...

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