Efficient Structure Learning of Bayesian networks Using Constraints

Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. Promising approaches have been reported, including automatic methods for facial and vocal affect recognition. Facial activities are characterized by three levels. First, in the bottom level, facial feature points around each facial component, i.e., eyebrow, mouth, capture the detailed face shape information. Second, in the middle level, facial action units, represent the contraction of a specific set of facial muscles, i.e., lid tightener,eyebrow raiser, etc.Finally,in the top level, six prototypical facial expressions represent facial muscle movement. A unified probabilistic framework based on the dynamic Bayesian network to simultaneously and coherently represent the facial evolvement in different levels, their interactions and their observations. Advanced machine learning methods are introduced to learn the model based on both training data and subjective prior knowledge.