Representation-based classification methods with enhanced linear reconstruction measures for face recognition. (October 2019)

• Record Type: Journal Article
• Authors: Gou, Jianping; Song, Jun; Ou, Weihua; Zeng, Shaoning; Yuan, Yunhao; Du, Lan
• Is Part Of: Computers & electrical engineering. Volume 79(2019)
• Publication Date: 2019-10
• Subjects: Collaborative representation -- Sparsity augmented collaborative representation -- Representation-based classification -- Linear reconstruction measurement; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.106451
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Propose the WELRMC method. Propose the TPWELRMC method. Design weighted sparsity augmented collaborative representation. Design coarse to fine augmented representation. Abstract: Representation-based classification (RBC) methods have recently been the promising pattern recognition technologies for object recognition. The representation coefficients of RBC as the linear reconstruction measure (LRM) can be well used for classifying objects. In this article, we propose two enhanced linear reconstruction measure-based classification methods based on the sparsity-augmented collaborative representation-based classification method (SA-CRC). One is the weighted enhancement linear reconstruction measure-based classification method (WELRMC) that introduces data localities into SA-CRC. Another is the two-phase weighted enhancement linear reconstruction measure-based classification method (TPWELRMC) that integrates both the coarse and fine representations into SA-CRC. To demonstrate the effectiveness of the proposed methods, experiments are conducted on several public face databases in comparison with the state-of-the-art representation-based classification methods. The experimental results show that the proposed methods significantly outperform the competing RBC methods.