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Conference Paper: A temporal latent topic model for facial expression recognition

TitleA temporal latent topic model for facial expression recognition
Authors
KeywordsBatch learning
Efficient learning
Facial expression recognition
Gibbs samplers
Image sequence
Issue Date2011
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
The 10th Asian Conference on Computer Vision (ACCV 2010), Queenstown, New Zealand, 8-12 November 2010. In Lecture Notes in Computer Science, 2010, v. 6495, p. 51-63 How to Cite?
AbstractIn this paper we extend the latent Dirichlet allocation (LDA) topic model to model facial expression dynamics. Our topic model integrates the temporal information of image sequences through redefining topic generation probability without involving new latent variables or increasing inference difficulties. A collapsed Gibbs sampler is derived for batch learning with labeled training dataset and an efficient learning method for testing data is also discussed. We describe the resulting temporal latent topic model (TLTM) in detail and show how it can be applied to facial expression recognition. Experiments on CMU expression database illustrate that the proposed TLTM is very efficient in facial expression recognition. © 2011 Springer-Verlag Berlin Heidelberg.
DescriptionLNCS v. 6495 is conference proceedings of the 10th Asian Conference on Computer Vision, Queens, ACCV
Posters: no. 128
Persistent Identifierhttp://hdl.handle.net/10722/142604
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorShang, Len_HK
dc.contributor.authorChan, KPen_HK
dc.date.accessioned2011-10-28T02:52:52Z-
dc.date.available2011-10-28T02:52:52Z-
dc.date.issued2011en_HK
dc.identifier.citationThe 10th Asian Conference on Computer Vision (ACCV 2010), Queenstown, New Zealand, 8-12 November 2010. In Lecture Notes in Computer Science, 2010, v. 6495, p. 51-63en_HK
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/142604-
dc.descriptionLNCS v. 6495 is conference proceedings of the 10th Asian Conference on Computer Vision, Queens, ACCV-
dc.descriptionPosters: no. 128-
dc.description.abstractIn this paper we extend the latent Dirichlet allocation (LDA) topic model to model facial expression dynamics. Our topic model integrates the temporal information of image sequences through redefining topic generation probability without involving new latent variables or increasing inference difficulties. A collapsed Gibbs sampler is derived for batch learning with labeled training dataset and an efficient learning method for testing data is also discussed. We describe the resulting temporal latent topic model (TLTM) in detail and show how it can be applied to facial expression recognition. Experiments on CMU expression database illustrate that the proposed TLTM is very efficient in facial expression recognition. © 2011 Springer-Verlag Berlin Heidelberg.en_HK
dc.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_HK
dc.relation.ispartofLecture Notes in Computer Scienceen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsThe original publication is available at www.springerlink.com-
dc.subjectBatch learning-
dc.subjectEfficient learning-
dc.subjectFacial expression recognition-
dc.subjectGibbs samplers-
dc.subjectImage sequence-
dc.titleA temporal latent topic model for facial expression recognitionen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailChan, KP:kpchan@cs.hku.hken_HK
dc.identifier.authorityChan, KP=rp00092en_HK
dc.description.naturepostprint-
dc.identifier.doi10.1007/978-3-642-19282-1_5en_HK
dc.identifier.scopuseid_2-s2.0-79952512807en_HK
dc.identifier.hkuros184439en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79952512807&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume6495 LNCSen_HK
dc.identifier.issuePART 4en_HK
dc.identifier.spage51en_HK
dc.identifier.epage63en_HK
dc.publisher.placeGermanyen_HK
dc.description.otherThe 10th Asian Conference on Computer Vision (ACCV 2010), Queenstown, New Zealand, 8-12 November 2010. In Lecture Notes in Computer Science, 2010, v. 6495, p. 51-63-
dc.identifier.scopusauthoridShang, L=55145022200en_HK
dc.identifier.scopusauthoridChan, KP=7406032820en_HK

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