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Conference Paper: Discriminative Hessian Eigenmaps for face recognition

TitleDiscriminative Hessian Eigenmaps for face recognition
Authors
KeywordsDimension reduction
Face recognition
Manifold learning
Issue Date2010
PublisherIEEE.
Citation
The 2010 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Dallas, TX., 14-19 March 2010. In IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, 2010, p. 5586-5589 How to Cite?
AbstractDimension reduction algorithms have attracted a lot of attentions in face recognition because they can select a subset of effective and efficient discriminative features in the face images. Most of dimension reduction algorithms can not well model both the intra-class geometry and interclass discrimination simultaneously. In this paper, we introduce the Discriminative Hessian Eigenmaps (DHE), a novel dimension reduction algorithm to address this problem. DHE will consider encoding the geometric and discriminative information in a local patch by improved Hessian Eigenmaps and margin maximization respectively. Empirical studies on public face database thoroughly demonstrate that DHE is superior to popular algorithms for dimension reduction, e.g., FLDA, LPP, MFA and DLA. ©2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/125723
ISSN
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorSi, Sen_HK
dc.contributor.authorTao, Den_HK
dc.contributor.authorChan, KPen_HK
dc.date.accessioned2010-10-31T11:48:08Z-
dc.date.available2010-10-31T11:48:08Z-
dc.date.issued2010en_HK
dc.identifier.citationThe 2010 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Dallas, TX., 14-19 March 2010. In IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, 2010, p. 5586-5589en_HK
dc.identifier.issn1520-6149en_HK
dc.identifier.urihttp://hdl.handle.net/10722/125723-
dc.description.abstractDimension reduction algorithms have attracted a lot of attentions in face recognition because they can select a subset of effective and efficient discriminative features in the face images. Most of dimension reduction algorithms can not well model both the intra-class geometry and interclass discrimination simultaneously. In this paper, we introduce the Discriminative Hessian Eigenmaps (DHE), a novel dimension reduction algorithm to address this problem. DHE will consider encoding the geometric and discriminative information in a local patch by improved Hessian Eigenmaps and margin maximization respectively. Empirical studies on public face database thoroughly demonstrate that DHE is superior to popular algorithms for dimension reduction, e.g., FLDA, LPP, MFA and DLA. ©2010 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE.-
dc.relation.ispartofICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedingsen_HK
dc.rightsIEEE International Conference on Acoustics, Speech and Signal Processing Proceedings. Copyright © IEEE.-
dc.rights©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectDimension reductionen_HK
dc.subjectFace recognitionen_HK
dc.subjectManifold learningen_HK
dc.titleDiscriminative Hessian Eigenmaps for face recognitionen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailChan, KP:kpchan@cs.hku.hken_HK
dc.identifier.authorityChan, KP=rp00092en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICASSP.2010.5495241en_HK
dc.identifier.scopuseid_2-s2.0-78049368921en_HK
dc.identifier.hkuros176312en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78049368921&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage5586en_HK
dc.identifier.epage5589en_HK
dc.identifier.isiWOS:000287096005125-
dc.publisher.placeUnited Statesen_HK
dc.description.otherThe 2010 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Dallas, TX., 14-19 March 2010. In IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings, 2010, p. 5586-5589-
dc.identifier.scopusauthoridSi, S=35422764200en_HK
dc.identifier.scopusauthoridTao, D=7102600334en_HK
dc.identifier.scopusauthoridChan, KP=7406032820en_HK

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