Robust and Practical Face Recognition via Structured Sparsity

Sparse representation based classification (SRC) methods have recently drawn much attention in face recognition, due to their good performance and robustness against misalignment, illumination variation, and occlusion. They assume the errors caused by image variations can be modeled as pixel-wisely sparse. However, in many practical scenarios these errors are not truly pixel-wisely sparse but rather sparsely distributed with structures, i.e., they constitute contiguous regions distributed at different face positions. In this paper, we introduce a class of structured sparsity-inducing norms into the SRC framework, to model various corruptions in face images caused by misalignment, shadow (due to illumination change), and occlusion. For practical face recognition, we develop an automatic face alignment method based on minimizing the structured sparsity norm. Experiments on benchmark face datasets show improved performance over SRC and other alternative methods.