Sparse shape registration for occluded facial feature localization

This paper proposes a sparsity driven shape registration method for occluded facial feature localization. Most current shape registration methods search landmark locations which comply both shape model and local image appearances. However, if the shape is partially occluded, the above goal is inappropriate and often leads to distorted shape results. In this paper, we introduce an error term to rectify the locations of the occluded landmarks. Under the assumption that occlusion takes a small proportion of the shape, we propose a sparse optimization algorithm that iteratively approaches the optimal shape. The experiments in our synthesized face occlusion database prove the advantage of our method.