Single-sample face recognition based on wssrc and expanding sample

Xu, Z; Ye, L; He, X

Single-sample face recognition based on wssrc and expanding sample

Xu, Z Ye, L He, X

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, 9426 pp. 197 - 206
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Xu, Z	en_US
dc.contributor.author	Ye, L	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, 9426 pp. 197 - 206	en_US
dc.identifier.isbn	9783319261805	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/41826
dc.description.abstract	© Springer International Publishing Switzerland 2015. This paper proposes a face recognition method with one training image per person, and it is based on compressed sensing. We apply nonlinear dimensionality reduction through locally linear embedding and sparse coefficients to generate multiple samples of each person. These generated samples have multi-expressions and multi-gestures are added to the original sample set for training. Then, a super sparse random projection and weighted optimization are applied to improve the SRC. This proposed method is named weighted super sparse representation classification (WSSRC) and is used for face recognition in this paper. Experiments on the well-known ORL face dataset and FERET face dataset show that WSSRC is about 15.53 % and 7.67 %, respectively, more accurate than the original SRC method in the context of single sample face recognition problem. In addition, extensive experimental results reported in this paper show that WSSRC also achieve higher recognition rates than RSRC, SSRC DMMA, and DCT-based DMMA.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-319-26181-2_18	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Single-sample face recognition based on wssrc and expanding sample	en_US
dc.type	Conference Proceeding
utslib.citation.volume	9426	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	080106 Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
dcterms.rights	The original publication is available at www.springerlink.com
pubs.publication-status	Published	en_US
pubs.volume	9426	en_US

Abstract:

© Springer International Publishing Switzerland 2015. This paper proposes a face recognition method with one training image per person, and it is based on compressed sensing. We apply nonlinear dimensionality reduction through locally linear embedding and sparse coefficients to generate multiple samples of each person. These generated samples have multi-expressions and multi-gestures are added to the original sample set for training. Then, a super sparse random projection and weighted optimization are applied to improve the SRC. This proposed method is named weighted super sparse representation classification (WSSRC) and is used for face recognition in this paper. Experiments on the well-known ORL face dataset and FERET face dataset show that WSSRC is about 15.53 % and 7.67 %, respectively, more accurate than the original SRC method in the context of single sample face recognition problem. In addition, extensive experimental results reported in this paper show that WSSRC also achieve higher recognition rates than RSRC, SSRC DMMA, and DCT-based DMMA.

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http://hdl.handle.net/10453/41826