Within the past decade, significant effort has occurred in developing methods of facial expression analysis. Because most investigators have used relatively limited data sets, the generalizability of these various methods remains unknown. We describe the problem space for facial expression analysis, which includes level of description, transitions among expression, eliciting conditions, reliability and validity of training and test data, individual differences in subjects, head orientation and scene complexity, image characteristics, and relation to non-verbal behavior. We then present the CMU-Pittsburgh AU-Coded Face Expression Image Database, which currently includes 2105 digitized image sequences from 182 adult subjects of varying ethnicity, performing multiple tokens of most primary FACS action units. This database is the most comprehensive test-bed to date for comparative studies of facial expression analysis. 1.

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior.

Abstract. Multimodal corpora prove useful at different stages of the development process of embodied conversational agents. Insights into human-human communicative behaviors can be drawn from such corpora. Rules for planning and generating such behavior in agents can be derived from this information. And even the evaluation of human-agent interactions can rely on corpus data from human-human communication. In this paper, we exemplify how corpora can be exploited at the different development steps, starting with the question of how corpora are annotated and on what level of granularity. The corpus data can be used either directly for imitating the human behavior recorded in the corpus or rules can be derived from the data which govern the behavior planning process. Corpora can even play a vital role in the evaluation of agent systems. Several studies are presented that make use of corpora for the evaluation task.

www.pitt.edu/~jeffcohn Abstract. Many people believe that emotions and subjective feelings are one and the same and that a goal of human-centered computing is emotion recognition. The first belief is outdated; the second mistaken. For humancentered computing to succeed, a different way of thinking is needed. Emotions are species-typical patterns that evolved because of their value in addressing fundamental life tasks. Emotions consist of multiple components, of which subjective feelings may be one. They are not directly observable, but inferred from expressive behavior, self-report, physiological indicators, and context. I focus on expressive facial behavior because of its coherence with other indicators and research. Among the topics included are measurement, timing, individual differences, dyadic interaction, and inference. I propose that design and implementation of perceptual user interfaces may be better informed by considering the complexity of emotion, its various indicators, measurement, individual differences, dyadic interaction, and problems of inference.