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论文类型：期刊论文

发表时间：2014-08-01

发表刊物：INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS

收录刊物：SCIE、EI、Scopus

卷号：5

期号：4

页面范围：599-605

ISSN号：1868-8071

关键字：Face recognition; Illumination variation; Shadow compensation; Robust PCA; Low-dimensional

摘要：Illumination variation that occurs on face images will significantly influence the performance of the recognition. Based on the low-dimensional intrinsic of face images, we design a novel two-step shadow compensation method for face recognition. Three indexes are proposed and employed to distinguish the shaded-images. Then, we compensate the shadows adaptively by using a modified Robust PCA result. Experimental results on Yale database and Yale B database demonstrate that the proposed approach can improve the recognition rate. Showing our method is suitable for face recognition with illumination variations.