Abstract
In this thesis, we propose two graphical models for automatically detecting facial features and estimating optical flow on face images for extracting the expression flow features. To accomplish these tasks, we apply the Loopy Belief Propagation (LBP) algorithm which is a common framework for graphical model. In the first part, we learn the feature PCA models and geometry relationship for building a graphical model for facial features. In the second part, we build a Markov Random Field (MRF) model for optical flow estimation, and the purpose of the model structure is to make sure that the patch of neutral image could move to correct corresponding position on the expression image. The local feature constraint makes the optical flow computation in the feature areas more precise. Finally, we combine these two algorithms with the SVM classifier to develop a facial expression recognition system.