Towards facial mimicry for a virtual human

Boukricha H, Wachsmuth I. Towards facial mimicry for a virtual human. In: Reichardt D, ed. Proceedings of the 4th Workshop on Emotion and Computing - Current Research and Future Impact. 2009: 32-39.Mimicking others’ facial expressions is believed to be important in making virtual humans as more natural and believable. As result of an empirical study conducted with a virtual human a large face repertoire of about 6000 faces arranged in Pleasure Arousal Dominance (PAD-) space with respect to two dominance values (dominant vs. submissive) was obtained. Each face in the face repertoire consists of different intensities of the virtual human’s facial muscle actions called Action Units (AUs), modeled following the Facial Action Coding System (FACS). Using this face repertoire an approach towards realizing facial mimicry for a virtual human is topic of this paper. A preliminary evaluation of this first approach is realized with the basic emotions Happy and Angry

Machine Understanding of Human Behavior

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

Aligning Figurative Paintings With Their Sources for Semantic Interpretation

This paper reports steps in probing the artistic methods of figurative painters through computational algorithms. We explore a comparative method that investigates the relation between the source of a painting, typically a photograph or an earlier painting, and the painting itself. A first crucial step in this process is to find the source and to crop, standardize and align it to the painting so that a comparison becomes possible. The next step is to apply different low-level algorithms to construct difference maps for color, edges, texture, brightness, etc. From this basis, various subsequent operations become possible to detect and compare features of the image, such as facial action units and the emotions they signify. This paper demonstrates a pipeline we have built and tested using paintings by a renowned contemporary painter Luc Tuymans. We focus in this paper particularly on the alignment process, on edge difference maps, and on the utility of the comparative method for bringing out the semantic significance of a painting

Machine Analysis of Facial Expressions

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