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 U+0028 FR U+0029 with single sample per person U+0028 SSPP U+0029. In the scenario of FR with SSPP, we present a novel model local robust sparse representation U+0028 LRSR U+0029 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 intraclass 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.