Facial event classification with task oriented dynamic Bayesian network

Facial events include all activities of face and facial features in spatial or temporal space, such as facial expressions, face gesture, gaze and furrow happening, etc. Developing an automated system for facial event classification is always a challenging task due to the richness, ambiguity and dynamic nature of facial expressions. This paper presents an efficient approach to real-world facial event classification. By integrating dynamic Bayesian network (DBN) with a general-purpose facial behavior description language, a task-oriented stochastic and temporal framework is constructed to systematically represent and classify facial events of interest. Based on the task oriented DBN, we can spatially and temporally incorporate results from previous times and prior knowledge of the application domain. With the top-down inference, the system can make active selection among multiple visual channels to identify the most effective sensory channels to use. With the bottom-up inference from observed evidences, the current facial event can be classified with a desired confident level via the belief propagation. We applied the task-oriented DBN framework to monitoring driver vigilance. Experimental results demonstrate the feasibility and efficiency of our approach.