Local Robust Sparse Representation for Face Recognition With Single Sample per Person

The purpose of this paper is to solve the problem of robust face recognition (FR) with single sample per person (SSPP). In the scenario of FR with SSPP, we present a novel model local robust sparse representation (LRSR) to tackle the problem of query images with various intra-class variations, e.g., expressions, illuminations, and occlusion. FR with SSPP is a very difficult challenge due to lacking of information to predict the possible intra-class variation of the query images. The key idea of the proposed method is to combine a local sparse representation model and a patch-based generic variation dictionary learning model to predict the possible facial intra-class variation of the query images. The experimental results on the AR database, Extended Yale B database, CMU-PIE database and LFW database show that the proposed method is robust to intra-class variations in FR with SSPP, and outperforms the state-of-art approaches.