Integrating Globality and Locality for Robust Representation Based Classification. (26th March 2014)

• Record Type: Journal Article
• Authors: Zhang, Zheng; Li, Zhengming; Xie, Binglei; Wang, Long; Chen, Yan
• Other Names: Proppe Carsten Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2014(2014)
• Publication Date: 2014-03-26
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2014/415856
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : 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.