Face Alignment Based on 3D Face Shape Model and 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 facial features are located by minimizing the potential function of markov random field, which is solved with dual decomposition. The main contribution of this paper is composed of three aspects. First, Random Project Tree is utilized to learn the manifold structure of the facial feature appearance under different view points. Second, a 3D face shape model is learned to capture the linear part of face shape distribution due to the change of identity and expression, which is served as the high order constraints. Third, Markov random field is introduced to model the nonlinear part of the face shape distribution, and also to deal with occlusion of facial features due to head pose variation or ornaments. Experiments was taken on the Texas 3D face database and face images downloaded from the Internet, results shows capability of adapting large head pose variations of the method.