CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2010 vol.32 Issue No.02 - February
Issue No.02 - February (2010 vol.32)
Yan Tong , GE Global Research Center, Niskayuna
Jixu Chen , Rensselaer Polytechnic Institute, Troy
Qiang Ji , Rensselaer Polytechnic Institute, Troy
Facial expression is a natural and powerful means of human communication. Recognizing spontaneous facial actions, however, is very challenging due to subtle facial deformation, frequent head movements, and ambiguous and uncertain facial motion measurements. Because of these challenges, current research in facial expression recognition is limited to posed expressions and often in frontal view. A spontaneous facial expression is characterized by rigid head movements and nonrigid facial muscular movements. More importantly, it is the coherent and consistent spatiotemporal interactions among rigid and nonrigid facial motions that produce a meaningful facial expression. Recognizing this fact, we introduce a unified probabilistic facial action model based on the Dynamic Bayesian network (DBN) to simultaneously and coherently represent rigid and nonrigid facial motions, their spatiotemporal dependencies, and their image measurements. 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, facial action recognition is accomplished through probabilistic inference by systematically integrating visual measurements with the facial action model. Experiments show that compared to the state-of-the-art techniques, the proposed system yields significant improvements in recognizing both rigid and nonrigid facial motions, especially for spontaneous facial expressions.
Facial action unit recognition, face pose estimation, facial action analysis, facial action coding system, Bayesian networks.
Yan Tong, Jixu Chen, Qiang Ji, "A Unified Probabilistic Framework for Spontaneous Facial Action Modeling and Understanding", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 2, pp. 258-273, February 2010, doi:10.1109/TPAMI.2008.293