We describe a computer vision system for observing facial motion by using an optimal estimation optical flow method coupled with geometric, physical and motion-based dynamic models describing the facial structure. Our method produces a reliable parametric representation of the face's independent muscle action groups, as well as an accurate estimate of facial motion. Previous efforts at analysis of facial expression have been based on the Facial Action Coding System (FACS), a representation developed in order to allow human psychologists to code expression from static pictures. To avoid use of this heuristic coding scheme, we have used our computer vision system to probabilistically characterize facial motion and muscle activation in an experimental population, thus deriving a new, more accurate representation of human facial expressions that we call FACS+. Finally, we show how this method can be used for coding, analysis, interpretation, and recognition of facial expressions.

Cross-cultural research on facial expression and the developments of methods to measure facial expression are briefly summarized. What has been learned about emotion from this work on the face is then elucidated. Four questions about facial expression and emotion are discussed. What information does an expression typically convey? Can there be emotion without facial expression? Can there be a facial expression of emotion without emotion? How do individuals differ in their facial expressions of emotion? In 1965 when 1 began to study facial expression, 1 few thought there was much to be learned. Goldstein (1981) pointed out that a number of famous psychologists—F. and G. Allport, Brunswik, Hull, Lindzey, Maslow, Osgood, Titchner—did only one facial study, which was not what earned them their reputations. Harold Schlosberg was an exception, but he was more interested in how to represent the information derived by those who observed the face than in expression itself. 2 The face was considered a meager source of mostly inaccurate, culturespecific, stereotypical information (Bruner & Tagiuri, 1954). That this contradicted what every layman knew made it all the more attractive. Psychology had exposed the falseness of a folk belief, a counterintuitive finding.

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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.

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Abstract — Spontaneous facial expressions differ from posed expressions in both which muscles are moved, and in the dynamics of the movement. Advances in the field of automatic facial expression measurement will require development and assessment on spontaneous behavior. Here we present preliminary results on a task of facial action detection in spontaneous facial expressions. We employ a user independent fully automatic system for real time recognition of facial actions from the Facial Action Coding System (FACS). The system automatically detects frontal faces in the video stream and coded each frame with respect to 20 Action units. The approach applies machine learning methods such as support vector machines and AdaBoost, to texture-based image representations. The output margin for the learned classifiers predicts action unit intensity. Frame-by-frame intensity measurements will enable investigations into facial expression dynamics which were previously intractable by human coding. I.

Emotions are universally recognized from facial expressions—or so it has been claimed. To support that claim, research has been carried out in various modern cultures and in cultures relatively isolated from Western influence. A review of the methods used in that research raises questions of its ecological, convergent, and internal validity. Forced-choice response format, within-subject design, preselected photographs of posed facial expressions, and other features of method are each problematic. When they are altered, less supportive or nonsupportive results occur. When they are combined, these method factors may help to shape the results. Facial expressions and emotion labels are probably associated, but the association may vary with culture and is loose enough to be consistent with various alternative accounts, 8 of which are discussed. "Everyone knows that grief involves a gloomy and joy a cheerful countenance.... There are characteristic facial expressions which are observed to accompany anger, fear, erotic excitement, and all the other passions " (Aristotle, nd/1913, pp. 805, 808). Aristotle was not proposing a new idea but was cataloging what was known on the topic of physiognomy. The theory was that a person's physical appearance, especially in the face, reveals deeper characteristics: Poor proportions reveal a rogue, soft hair a coward, and a smile a happy person.' Today, few psychologists share Aristotle's belief about the meaning of poor proportions or soft hair, but many share his beliefs about facial expression and emotion. Oatley and Jenkins (1992) observed, "By far the most extensive body of data in the field of human emotions is that on facial expressions of emotion" (p. 67). Recent reviews of those data (see Table 1) agree that the face reveals emotion in a way that is universally understood: Happiness, surprise, fear, anger, contempt, disgust, and sadness—these seven emotions, plus or minus two, are recognized from facial expressions by all human beings, regardless of their cultural background.

This paper introduces Gesture Engine, an animation system that synthesizes human gesturing behaviors from augmented conversation transcripts using a database of highlevel gesture definitions. An abstract scripting language to specify hand-arm gestures is introduced that incorporates knowledge from sign language research, psycholinguistics, and traditional keyframe animation. A new planning algorithm instantiates and adjusts gestures according to communicative context and temporal constraints obtained from a speech synthesizer. The system animates an MPEG-4 compliant skeleton using Body Animation Parameters. 1

In the paper we present two novel interactive tools, Emotion Disc and Emotion Squares, to explore the facial expression space. They map navigation in a 2D circle, by the first tool, or in two 2D squares, by the second tool, to the highdimensional parameter space of facial expressions, by using a small number of predefined reference expressions. They can be used as exploration tools by researchers, or as control devices by end-users to put expressions on the face of embodied agents or avatars in applications like games, telepresence and education.

ion Signal Level Semantic Level Figure 13: Multimodal Design Space (adapted from [224]) system in the design space is the pivotal center of its features. According to the characterization of an interaction along the two dimensions, fusion, and use of modalities, four basic types of multimodal interactions can be distinguished: alternative, synergistic, exclusive, and concurrent multimodal interaction, as shown in Figure 13. Obviously, synergistic systems subsume the other three classes of multimodal systems. Therefore, architectural models of multimodal integration (as presented in the next subsection and in Section 9) are sufficient if they are able to model synergistic cooperation of modalities. 6.2.2 Fusion of Multimodal Input Fusion of multimodal input events can occur on different levels, ranging from signal-level to semantic-level. Signal-level fusion (or lexical fusion [224]) performs the combination of multimodal input at the level of the input signal. Signal-level fusion has...