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Occlusion face recognition based on robust principal component analysis and low rank(PDF)


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Occlusion face recognition based on robust principal component analysis and low rank
Tang XianHuang Junwei
Department of Cmputer Engineering,Shangqiu University,Shangqiu 476000,China
robust principal component analysis pattern recognition occlusion face low rank mapping error rate
In order to improve the recognition accuracy of occlusion face,a novel occlusion face recognition algorithm combining robust principal component analysis and low rank is proposed.Firstly,face images are collected and are correspondingly pretreated,and secondly the face samples are decomposed by using robust principal component analysis to obtain low rank data matrix and sparse error matrix,and face images of training samples and testing samples are established.At last face is weighted and recognized according to the error matrix,the classic face database is used to carried out simulation experiment.The results show that the proposed algorithm has improved the occluded face recognition accuracy significantly,effectively reduces the error rate of the occluded face,and has better robustness.


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Last Update: 2017-08-31