Face alignment based on high order markov random field

This paper presents a novel method for face alignment under unknown head poses and nonrigid warp, within the framework of Markov random field. The proposed method learns a 3D face shape model comprised of 31 facial features and a texture model for each facial feature from a 3D face database. The models are combined to serve as the unary, pairwise and high order constraints of the Markov random field. The face images are aligned by minimizing the potential function of the Markov random field, which is solved with dual decomposition. Results of experiments which were taken on the Texas 3D face database and PIE face database show the robustness of the proposed method to large head pose and illumination variations.