17 January 2005 Information processing of motion in facial expression and the geometry of dynamical systems
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An interesting problem in analysis of video data concerns design of algorithms that detect perceptually significant features in an unsupervised manner, for instance methods of machine learning for automatic classification of human expression. A geometric formulation of this genre of problems could be modeled with help of perceptual psychology. In this article, we outline one approach for a special case where video segments are to be classified according to expression of emotion or other similar facial motions. The encoding of realistic facial motions that convey expression of emotions for a particular person P forms a parameter space XP whose study reveals the “objective geometry” for the problem of unsupervised feature detection from video. The geometric features and discrete representation of the space XP are independent of subjective evaluations by observers. While the “subjective geometry” of XP varies from observer to observer, levels of sensitivity and variation in perception of facial expressions appear to share a certain level of universality among members of similar cultures. Therefore, statistical geometry of invariants of XP for a sample of population could provide effective algorithms for extraction of such features. In cases where frequency of events is sufficiently large in the sample data, a suitable framework could be provided to facilitate the information-theoretic organization and study of statistical invariants of such features. This article provides a general approach to encode motion in terms of a particular genre of dynamical systems and the geometry of their flow. An example is provided to illustrate the general theory.
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Amir H. Assadi, Amir H. Assadi, Hamid Eghbalnia, Hamid Eghbalnia, Brenton W. McMenamin, Brenton W. McMenamin, } "Information processing of motion in facial expression and the geometry of dynamical systems", Proc. SPIE 5675, Vision Geometry XIII, (17 January 2005); doi: 10.1117/12.586073; https://doi.org/10.1117/12.586073


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