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Conference Paper: Nonparametric discriminant HMM and application to facial expression recognition

TitleNonparametric discriminant HMM and application to facial expression recognition
Authors
KeywordsAdaptive kernels
Class level
Discrimination ability
Expectation-maximization method
Facial expression recognition
Issue Date2009
PublisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000147
Citation
2009 Ieee Computer Society Conference On Computer Vision And Pattern Recognition Workshops, Cvpr Workshops 2009, 2009, p. 2090-2096 How to Cite?
AbstractThis paper presents a nonparametric discriminant HMM and applies it to facial expression recognition. In the proposed HMM, we introduce an effective nonparametric output probability estimation method to increase the discrimination ability at both hidden state level and class level. The proposed method uses a nonparametric adaptive kernel to utilize information from all classes and improve the discrimination at class level. The discrimination between hidden states is increased by defining membership coefficients which associate each reference vector with hidden states. The adaption of such coefficients is obtained by the Expectation Maximization (EM) method. Furthermore, we present a general formula for the estimation of output probability, which provides a way to develop new HMMs. Finally, we evaluate the performance of the proposed method on the CMU expression database and compare it with other nonparametric HMMs. © 2009 IEEE.
DescriptionProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, p. 2090-2096
Persistent Identifierhttp://hdl.handle.net/10722/61189
ISBN
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorShang, Len_HK
dc.contributor.authorChan, KPen_HK
dc.date.accessioned2010-07-13T03:32:49Z-
dc.date.available2010-07-13T03:32:49Z-
dc.date.issued2009en_HK
dc.identifier.citation2009 Ieee Computer Society Conference On Computer Vision And Pattern Recognition Workshops, Cvpr Workshops 2009, 2009, p. 2090-2096en_HK
dc.identifier.isbn978-1-4244-3991-1-
dc.identifier.issn1063-6919-
dc.identifier.urihttp://hdl.handle.net/10722/61189-
dc.descriptionProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009, p. 2090-2096en_HK
dc.description.abstractThis paper presents a nonparametric discriminant HMM and applies it to facial expression recognition. In the proposed HMM, we introduce an effective nonparametric output probability estimation method to increase the discrimination ability at both hidden state level and class level. The proposed method uses a nonparametric adaptive kernel to utilize information from all classes and improve the discrimination at class level. The discrimination between hidden states is increased by defining membership coefficients which associate each reference vector with hidden states. The adaption of such coefficients is obtained by the Expectation Maximization (EM) method. Furthermore, we present a general formula for the estimation of output probability, which provides a way to develop new HMMs. Finally, we evaluate the performance of the proposed method on the CMU expression database and compare it with other nonparametric HMMs. © 2009 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000147en_HK
dc.relation.ispartof2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009en_HK
dc.rightsIEEE Conference on Computer Vision and Pattern Recognition. Proceedings. Copyright © IEEE Computer Society.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2009 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.subjectAdaptive kernels-
dc.subjectClass level-
dc.subjectDiscrimination ability-
dc.subjectExpectation-maximization method-
dc.subjectFacial expression recognition-
dc.titleNonparametric discriminant HMM and application to facial expression recognitionen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=978-1-4244-3991-1&volume=&spage=2090&epage=2096&date=2009&atitle=Nonparametric+discriminant+HMM+and+application+to+facial+expression+recognition-
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/CVPRW.2009.5206509en_HK
dc.identifier.scopuseid_2-s2.0-70450159420en_HK
dc.identifier.hkuros161860en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-70450159420&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage2090en_HK
dc.identifier.epage2096en_HK
dc.identifier.scopusauthoridShang, L=55145022200en_HK
dc.identifier.scopusauthoridChan, KP=7406032820en_HK

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