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Research Articles

Determination of candidate subjects for better recognition of faces

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Pages 982-988 | Received 04 Feb 2015, Accepted 22 Jul 2015, Published online: 14 Sep 2015
 

Abstract

In order to improve the accuracy of face recognition and to solve the problem of various poses, we present an improved collaborative representation classification (CRC) algorithm using original training samples and the corresponding mirror images. First, the mirror images are generated from the original training samples. Second, both original training samples and their mirror images are simultaneously used to represent the test sample via improved collaborative representation. Then, some classes which are “close” to the test sample are coarsely selected as candidate classes. At last, the candidate classes are used to represent the test sample again, and then the class most similar to the test sample can be determined finely. The experimental results show our proposed algorithm has more robustness than the original CRC algorithm and can effectively improve the accuracy of face recognition.

Additional information

Funding

Research by Xuansheng Wang was supported in part by the Shenzhen infrastructure [project No. JCYJ20130401160945589]; Zhen Chen research was supported in part by NSFC [grant number 11201092]; the Science and Technology Foundation of Guizhou province [No. J[2013]2207]; Zhongming Teng research was supported in part by Natural Science Foundation of Fujian province [No. 2015J01580].

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