Simultaneous facial activity tracking and recognition

Yongqiang Li, Yongping Zhao, Qiang Ji

Abstract: Facial feature tracking and facial actions recognition from image sequence attracted great attention in computer vision field. Most current methods treat them as independent problems, hence ignore the interactions between facial feature points and facial actions. In this paper, we introduce a probabilistic framework based on the Dynamic Bayesian network (DBN) to simultaneously and coherently represent the facial evolvement in different levels, their interactions and their observations. Given the model and the measurements of facial motions, both facial features and facial actions are simultaneously recognized through probabilistic inference. Experiments show that compared to the state-of-the-art techniques, the proposed model can improve both the tracking and recognition performance.