Structured Regularized Robust Coding for Face Recognition

Meng Yang, Tiancheng Song, Feng Liu, Linlin Shen

Abstract: The sparse representation based classifier (SRC) has been successfully applied to robust face recognition (FR) with various variations. To achieve much stronger robustness to facial occlusion, recently regularized robust coding (RRC) was proposed by designing a new robust representation residual term. Although RRC has achieved the leading performance, it ignores the structured information (i.e., spatial consistence) embedded in the occluded pixels. In this paper, we proposed a novel structured regularized robust coding (SRRC) framework, in which the spatial consistence of occluded pixels was exploited by pixel weight learning (PWL) model. Efficient algorithms were also proposed to fastly learn the pixel’s weight and accurately recover the occluded area. The experiments on face recognition in several representative datasets clearly show the advantage of the proposed SRRC in accuracy and efficiency.