Integrating affinity propagation clustering method with linear discriminant analysis for face recognition

Du, C; Yang, J; Wu, Q; Li, F

Integrating affinity propagation clustering method with linear discriminant analysis for face recognition

Du, C Yang, J Wu, Q

Li, F

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Publication Type:: Journal Article
Citation:: Optical Engineering, 2007, 46 (11)
Issue Date:: 2007-12-10

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Field	Value	Language
dc.contributor.author	Du, C	en_US
dc.contributor.author	Yang, J	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Li, F	en_US
dc.date.issued	2007-12-10	en_US
dc.identifier.citation	Optical Engineering, 2007, 46 (11)	en_US
dc.identifier.issn	0091-3286	en_US
dc.identifier.uri	http://hdl.handle.net/10453/3209
dc.description.abstract	The Fisherface method suffers from the problem of using all training face images to recognize a test face image. To tackle this problem, we propose combining a novel clustering method, affinity propagation (AP), recently reported in the journal Science, with linear discriminant analysis (LDA) to form a new method, AP-LDA, for face recognition. By using AP, a representative face image for each subject can be obtained. Our AP-LDA method uses only these representative face images rather than all training images for recognition. Thus, it is more computationally efficient than Fisherface. Experimental results on several benchmark face databases also show that AP-LDA outperforms Fisherface in terms of recognition rate. © 2007 Society of Photo-Optical Instrumentation Engineers.	en_US
dc.relation.ispartof	Optical Engineering	en_US
dc.relation.isbasedon	10.1117/1.2801735	en_US
dc.subject.classification	Optics	en_US
dc.title	Integrating affinity propagation clustering method with linear discriminant analysis for face recognition	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.citation.volume	46	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0205 Optical Physics	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	ISI:000251549300002	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 - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	open_access
pubs.issue	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	46	en_US

Abstract:

The Fisherface method suffers from the problem of using all training face images to recognize a test face image. To tackle this problem, we propose combining a novel clustering method, affinity propagation (AP), recently reported in the journal Science, with linear discriminant analysis (LDA) to form a new method, AP-LDA, for face recognition. By using AP, a representative face image for each subject can be obtained. Our AP-LDA method uses only these representative face images rather than all training images for recognition. Thus, it is more computationally efficient than Fisherface. Experimental results on several benchmark face databases also show that AP-LDA outperforms Fisherface in terms of recognition rate. © 2007 Society of Photo-Optical Instrumentation Engineers.

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http://hdl.handle.net/10453/3209