Robust Modular Linear Regression Based Classification for Face Recognition with Occlusion

Face recognition with occlusion is a challenging problem. Recently, the modular representation based method, i.e., modular linear regression based classification (MLRC) was proposed to deal with this problem. However, MLRC just simply combines the individual decision of each block within an image (based on the min rule) to make final decision. Therefore, the block distance information is not fully exploited. In this paper, we propose a robust modular linear regression based classification (RMLRC) method to overcome the above problem. RMLRC can effectively fuse the information provided by all the blocks and thus alleviate the limiations of the MLRC method. Experimental results show that the RMLRC method can achieve promising results for face recognition with occlusion.