Sparse error via reweighted Low Rank Representation for face recognition with various illumination and occlusion

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• 作者：Guoliang Yang ; ^{ygliang30@126.com" class="auth_mail" title="E-mail the corresponding author} ; Yiqin Feng ^{feng5253995253@163.com" class="auth_mail" title="E-mail the corresponding author} ; Hairong Lu ^{1248278429@qq.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Face recognition ; Reweighted Low Rank Representation ; Low rank projection ; Sparse error image
• 刊名：Optik - International Journal for Light and Electron Optics
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：126
• 期：24
• 页码：5376-5380
• 全文大小：1277 K

文摘

In this paper, we present a novel algorithm which use sparse error via reweighted Low Rank Representation (SE_RLRR) for face recognition with various illumination and occlusion. The SE_RLRR is divided into two steps: The first step is to construct low rank projection for each type of training samples with RLRR. The second step is to calculate sparse error matrix on low rank projection for test sample and then classify the test sample with classification criterion which fuse smoothness with edge information of sparse error matrix using weighted sum rules. Experiment results on face database of AR and Extended Yale B confirm that our method is robust to various illumination and occlusion, and has better recognition rate than many other methods.