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Superimposed Sparse Parameter Classifiers for Face Recognition
Feng, Qingxiang1,2; Yuan, Chun1; Pan, Jeng-Shyang3; Yang, Jar-Ferr4; Chou, Yang-Ting4; Zhou, Yicong2; Li, Weifeng1
2017-02
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
Volume47Issue:2Pages:378-390
Abstract

In this paper, a novel classifier, called superimposed sparse parameter (SSP) classifier is proposed for face recognition. SSP is motivated by two phase test sample sparse representation (TPTSSR) and linear regression classification (LRC), which can be treated as the extended of sparse representation classification (SRC). SRC uses all the train samples to produce the sparse representation vector for classification. The LRC, which can be interpreted as L2-norm sparse representation, uses the distances between the test sample and the class subspaces for classification. TPTSSR is also L2-norm sparse representation and uses two phase to compute the distance for classification. Instead of the distances, the SSP classifier employs the SSPs, which can be expressed as the sum of the linear regression parameters of each class in iterations, is used for face classification. Further, the fast SSP (FSSP) classifier is also suggested to reduce the computation cost. A mass of experiments on Georgia Tech face database, ORL face database, CVL face database, AR face database, and CASIA face database are used to evaluate the proposed algorithms. The experimental results demonstrate that the proposed methods achieve better recognition rate than the LRC, SRC, collaborative representation-based classification, regularized robust coding, relaxed collaborative representation, support vector machine, and TPTSSR for face recognition under various conditions.

KeywordFace Recognition Linear Regression Sparse Representation Two Phase Sparse Representation
DOI10.1109/TCYB.2016.2516239
URLView the original
Indexed BySCI
Language英语
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000395476200010
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
The Source to ArticleWOS
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Citation statistics
Cited Times [WOS]:24   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Graduate School at Shenzhen, Tsinghua University, Shenzhen 518000, China
2.Department of Computer and Information Science, University of Macau, Macau 999078, China
3.Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518000, China
4.Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Feng, Qingxiang,Yuan, Chun,Pan, Jeng-Shyang,et al. Superimposed Sparse Parameter Classifiers for Face Recognition[J]. IEEE TRANSACTIONS ON CYBERNETICS,2017,47(2):378-390.
APA Feng, Qingxiang.,Yuan, Chun.,Pan, Jeng-Shyang.,Yang, Jar-Ferr.,Chou, Yang-Ting.,...&Li, Weifeng.(2017).Superimposed Sparse Parameter Classifiers for Face Recognition.IEEE TRANSACTIONS ON CYBERNETICS,47(2),378-390.
MLA Feng, Qingxiang,et al."Superimposed Sparse Parameter Classifiers for Face Recognition".IEEE TRANSACTIONS ON CYBERNETICS 47.2(2017):378-390.
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