Robust Principal Component Analysis and Low-Rank Projection for Face Recognition with Occlusion
Lei Zeng · Jisuanji fangzhen · 2015
Traditional face recognition method which affected by varying illumination,occlusion,disguise,as well as shadow and noise,runs an unstable performance. In this paper,we proposed an algorithm which combined Robust Principal Component Analysis( RPCA) and low-rank projection for face recognition. We divided each type of training sample into sum of low-rank matrix and sparse error matrix by RPCA,and constructed low-rank projection between training face matrix and the separated low-rank matrix. Then each test face image can obtain a low- rank matrix and a sparse error matrix corresponding to each face category by low-rank projection. In order to obtain discriminating information of the sparse error matrix,we calculated the smoothness and carried out the edge detection for the sparse error image,what is more,we made sum of weighted smoothness and edge information a criterion for classification. Experiment results based on face database of AR and Extended Yale B testify the effectiveness of the proposed method with an improved recognition rate.