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