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A Unified Probabilistic Framework for Facial Activity Modeling and Understanding

Facial activities are the most natural and powerful means of human communication. Spontaneous facial activity is characterized by rigid head movements, non-rigid facial muscular movements, and their interactions. Current research in facial activity analysis is limited to recognizing rigid or non-rigid motion separately, often ignoring their interactions. Furthermore, although some of them analyze the temporal properties of facial features during facial feature extraction, they often recognize the facial activity statically, ignoring the dynamics of the facial activity. In this paper, we propose to explicitly exploit the prior knowledge about facial activities and systematically combine the prior knowledge with image measurements to achieve an accurate, robust, and consistent facial activity understanding. Specifically, we propose a unified probabilistic framework based on the dynamic Bayesian network (DBN) to simultaneously and coherently represent the rigid and non-rigid facial motions, their interactions, and their image observations, as well as to capture the temporal evolution of the facial activities. Robust computer vision methods are employed to obtain measurements of both rigid and non-rigid facial motions. Finally, facial activity recognition is accomplished through a probabilistic inference by systemically integrating the visual measurements with the facial activity model.