LAUSR

Abstract Face synthesis has become a highly challenging task due to illumination, face expression variation and occlusion. The point of such technique is to efficiently represent face. Recently, sparse component analysis and parts-based representation are two widely used paradigms for face representation. In this paper, we propose a probabilistic generative model for face representation towards face synthesis, which simultaneously takes advantage of the robustness of sparse component analysis and the flexibility of parts-based representation. For a given image, we project the image on the trained model and obtain the projection coefficients. Finally, a new face is reconstructed according to the learned model and projection coefficients. This model is driven by data and is a function over hidden variable and model parameters in essence. As a result, it is specifically good at representing face images. The learned face parts prior is reasonable, continuous and flexible. To validate the eff ; ;ectiveness of the proposed method on face synthesis, we perform experiments in two applications: face restoration and learning to smile. The experimental results show its advantages.