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Sample diversity, representation effectiveness and robust dictionary learning for face recognition
Xu, Yong1,2; Li, Zhengming1,3; Zhang, Bob4; Yang, Jian5; You, Jane6
2017-01
Source PublicationINFORMATION SCIENCES
ISSN0020-0255
Volume375Pages:171-182
Abstract

Conventional dictionary learning algorithms suffer from the following problems when applied to face recognition. First, since in most face recognition applications there are only a limited number of original training samples, it is difficult to obtain a reliable dictionary with a large number of atoms from these samples. Second, because the face images of the same person vary with facial poses and expressions as well as illumination conditions, it is difficult to obtain a robust dictionary for face recognition. Thus, obtaining a robust and reliable dictionary is a crucial key to improve the performance of dictionary learning algorithms for face recognition. In this paper, we propose a novel dictionary learning framework to achieve this. The proposed algorithm framework takes training sample diversities of the same face image into account and tries to obtain more effective representations of face images and a more robust dictionary. It first produces virtual face images and then designs an elaborate objective function. Based on this objective function, we obtain a mathematically tractable and computationally efficient algorithm to generate a robust dictionary. Experimental results demonstrate that the proposed algorithm framework outperforms some previous state-of-the-art dictionary learning and sparse coding algorithms in face recognition. Moreover, the proposed algorithm framework can also be applied to other pattern classification tasks.

KeywordDictionary Learning Sparse Coding Face Recognition
DOI10.1016/j.ins.2016.09.059
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000387518200011
PublisherELSEVIER SCIENCE INC
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:50   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorXu, Yong
Affiliation1.Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China
2.Shenzhen Key Laboratory of Network Oriented Intelligent Computation, Shenzhen, China
3.Industrial Training Center, Guangdong Polytechnic Normal University, Guangzhou 510665, China
4.Department of Computer and Information Science, University of Macau, Avenida da Universidade, Taipa, Macau, China
5.School of Computer Science & Technology, Nanjing University of Science & Technology, Nanjing, China
6.Biometrics Researcher Centre, Department of Computing, Hong Kong Polytechnic University, Hong Kong, China
Recommended Citation
GB/T 7714
Xu, Yong,Li, Zhengming,Zhang, Bob,et al. Sample diversity, representation effectiveness and robust dictionary learning for face recognition[J]. INFORMATION SCIENCES,2017,375:171-182.
APA Xu, Yong,Li, Zhengming,Zhang, Bob,Yang, Jian,&You, Jane.(2017).Sample diversity, representation effectiveness and robust dictionary learning for face recognition.INFORMATION SCIENCES,375,171-182.
MLA Xu, Yong,et al."Sample diversity, representation effectiveness and robust dictionary learning for face recognition".INFORMATION SCIENCES 375(2017):171-182.
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