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Mathematical Problems in Engineering
Volume 2014, Article ID 415856, 10 pages
Research Article

Integrating Globality and Locality for Robust Representation Based Classification

1Key Laboratory of Network Oriented Intelligent Computation, Shenzhen, Guangdong 518055, China
2Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
3Guangdong Industry Training Center, Guangdong Polytechnic Normal University, Guangzhou, Guangdong 510665, China
4Shenzhen Sunwin Intelligent Corporation, Shenzhen, Guangdong 518055, China

Received 24 December 2013; Accepted 21 February 2014; Published 26 March 2014

Academic Editor: Carsten Proppe

Copyright © 2014 Zheng Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The representation based classification method (RBCM) has shown huge potential for face recognition since it first emerged. Linear regression classification (LRC) method and collaborative representation classification (CRC) method are two well-known RBCMs. LRC and CRC exploit training samples of each class and all the training samples to represent the testing sample, respectively, and subsequently conduct classification on the basis of the representation residual. LRC method can be viewed as a “locality representation” method because it just uses the training samples of each class to represent the testing sample and it cannot embody the effectiveness of the “globality representation.” On the contrary, it seems that CRC method cannot own the benefit of locality of the general RBCM. Thus we propose to integrate CRC and LRC to perform more robust representation based classification. The experimental results on benchmark face databases substantially demonstrate that the proposed method achieves high classification accuracy.