Enhanced kernel minimum squared error algorithm and its application in face recognition

Zhao, Y; He, X; Chen, B; Zhao, X

Enhanced kernel minimum squared error algorithm and its application in face recognition

Zhao, Y He, X

Chen, B Zhao, X

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Publication Type:: Journal Article
Citation:: Journal of Southeast University (English Edition), 2016, 32 (1), pp. 35 - 38
Issue Date:: 2016-03-01

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Field	Value	Language
dc.contributor.author	Zhao, Y	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Chen, B	en_US
dc.contributor.author	Zhao, X	en_US
dc.date.issued	2016-03-01	en_US
dc.identifier.citation	Journal of Southeast University (English Edition), 2016, 32 (1), pp. 35 - 38	en_US
dc.identifier.issn	1003-7985	en_US
dc.identifier.uri	http://hdl.handle.net/10453/44013
dc.description.abstract	© 2016, Editorial Department of Journal of Southeast University. All right reserved. To improve the classification performance of the kernel minimum squared error (KMSE), an enhanced KMSE algorithm (EKMSE) is proposed. It redefines the regular objective function by introducing a novel class label definition, and the relative class label matrix can be adaptively adjusted to the kernel matrix. Compared with the common methods, the new objective function can enlarge the distance between different classes, which therefore yields better recognition rates. In addition, an iteration parameter searching technique is adopted to improve the computational efficiency. The extensive experiments on FERET and GT face databases illustrate the feasibility and efficiency of the proposed EKMSE. It outperforms the original MSE, KMSE, some KMSE improvement methods, and even the sparse representation-based techniques in face recognition, such as collaborate representation classification (CRC).	en_US
dc.relation.ispartof	Journal of Southeast University (English Edition)	en_US
dc.relation.isbasedon	10.3969/j.issn.1003-7985.2016.01.007	en_US
dc.subject.classification	General Science & Technology	en_US
dc.title	Enhanced kernel minimum squared error algorithm and its application in face recognition	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	32	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/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic 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	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	32	en_US

Abstract:

© 2016, Editorial Department of Journal of Southeast University. All right reserved. To improve the classification performance of the kernel minimum squared error (KMSE), an enhanced KMSE algorithm (EKMSE) is proposed. It redefines the regular objective function by introducing a novel class label definition, and the relative class label matrix can be adaptively adjusted to the kernel matrix. Compared with the common methods, the new objective function can enlarge the distance between different classes, which therefore yields better recognition rates. In addition, an iteration parameter searching technique is adopted to improve the computational efficiency. The extensive experiments on FERET and GT face databases illustrate the feasibility and efficiency of the proposed EKMSE. It outperforms the original MSE, KMSE, some KMSE improvement methods, and even the sparse representation-based techniques in face recognition, such as collaborate representation classification (CRC).

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http://hdl.handle.net/10453/44013