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Research Article Open Access
Facial feature tracking and facial actions recognition from image sequence involved great awareness in computer vision field. In this paper, Facial activities are describe by three levels: primary, in the base level, facial element points about each facial component, i.e., eyebrow, mouth, etc, capture the full face outline information; next, in the center level, facial action units (AUs), clear in Facial Action Coding method, represent the contraction of a specific set of facial muscles, i.e., lid tightener, eyebrow raiser, etc; to finish, in the top level, six prototypical facial expressions represent the overall facial muscle movement and are usually used to describe the human emotion state. this paper introduces a joined probabilistic structure based on the Dynamic Bayesian network (DBN) to simultaneously and logically 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. Given the model and the measurements of facial motions, all three levels of facial activities are simultaneously recognized through a probabilistic inference. Extensive experiments are performed to illustrate the feasibility and effectiveness of the proposed model on all three level facial activities.