Discrminative geometry preserving projections

Song, D; Tao, D

Discrminative geometry preserving projections

Song, D Tao, D

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Image Processing, ICIP, 2009, pp. 2457 - 2460
Issue Date:: 2009-01-01

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Field	Value	Language
dc.contributor.author	Song, D	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2009-01-01	en_US
dc.identifier.citation	Proceedings - International Conference on Image Processing, ICIP, 2009, pp. 2457 - 2460	en_US
dc.identifier.isbn	9781424456543	en_US
dc.identifier.issn	1522-4880	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17273
dc.description.abstract	Dimension reduction algorithms have attracted a lot of attentions in face recognition and human gait recognition because they can select a subset of effective and efficient discriminative features. In this paper, we apply the Discriminative Geometry Preserving Projections (DGPP), a new subspace learning algorithm to address these problems. DGPP models both the intraclass geometry and interclass discrimination. Meanwhile, DGPP will not meet the undersampled problem. Thoroughly empirical studies on YALE face database, UMIST face database, FERET face database and USF Human-ID gait database demonstrate that DGPP is superior the popular algorithms for dimension reduction, e.g., PCA, LDA, NPE and LPP. ©2009 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Image Processing, ICIP	en_US
dc.relation.isbasedon	10.1109/ICIP.2009.5414091	en_US
dc.title	Discrminative geometry preserving projections	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Cairo, Egypt	en_US
dc.location.activity	Washington, DC, USA
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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Dimension reduction algorithms have attracted a lot of attentions in face recognition and human gait recognition because they can select a subset of effective and efficient discriminative features. In this paper, we apply the Discriminative Geometry Preserving Projections (DGPP), a new subspace learning algorithm to address these problems. DGPP models both the intraclass geometry and interclass discrimination. Meanwhile, DGPP will not meet the undersampled problem. Thoroughly empirical studies on YALE face database, UMIST face database, FERET face database and USF Human-ID gait database demonstrate that DGPP is superior the popular algorithms for dimension reduction, e.g., PCA, LDA, NPE and LPP. ©2009 IEEE.

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