Abstract
Three-dimensional human face modeling is a very popular topic with many applications,such as facial animation, face recognition and model-based facial video communication. Therefore, how to model the facial geometry, texture intensity and illumination variation is important in computer vision and graphics. Previous works on 3D head modeling from a single face image utilized prior information on 3D head models. However, it is difficult to accurately reconstruct the 3D face model from a single face image with expression since the facial expression induces 3D face model deformation in a complex manner. The main challenge is the coupling of the neutral 3D face model and the 3D deformation due to expression, thus making the 3D model estimation from a single face image with expression very challenging. On the other hand, the illumination condition also makes the problem more difficult. In this thesis,we focus on developing a 3D face model reconstruction system including surface registration and training of 3D face models with expressional deformations as well as the estimation of the 3D neutral shape and the 3D expressional deformation from a single face image. The proposed reconstruction algorithm integrates the linear and non-linear subspace representations for a prior 3D neutral morphable model and the probabilistic manifold-based 3D expressional deformation. We incorporate the face geometry, expression deformation, texture and illumination information into the problem so that it is well constrained. The reconstructed 3D face models can also be further extended and applied to many real-world applications.