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In this paper an accurate real-time sequence-based system for representation,recognition, interpretation, and analysis of the facial action units (AUs) andexpressions is presented. Our system has the following characteristics: 1)employing adaptive-network-based fuzzy inference systems (ANFIS) and temporalinformation, we developed a classification scheme based on neuro-fuzzy modelingof the AU intensity, which is robust to intensity variations, 2) using bothgeometric and appearance-based features, and applying efficient dimensionreduction techniques, our system is robust to illumination changes and it canrepresent the subtle changes as well as temporal information involved information of the facial expressions, and 3) by continuous values of intensityand employing top-down hierarchical rule-based classifiers, we can developaccurate human-interpretable AU-to-expression converters. Extensive experimentson Cohn-Kanade database show the superiority of the proposed method, incomparison with support vector machines, hidden Markov models, and neuralnetwork classifiers. Keywords: biased discriminant analysis (BDA), classifierdesign and evaluation, facial action units (AUs), hybrid learning, neuro-fuzzymodeling.

Analysis, Interpretation, and Recognition of Facial Action Units and Expressions Using Neuro-Fuzzy Modeling