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Conference Paper: Temporal Exemplar-based Bayesian Networks for facial expression recognition

TitleTemporal Exemplar-based Bayesian Networks for facial expression recognition
Authors
KeywordsDistributed parameter networks
Face recognition
Gesture recognition
Inference engines
Intelligent networks
Issue Date2008
PublisherIEEE.
Citation
Proceedings - 7Th International Conference On Machine Learning And Applications, Icmla 2008, 2008, p. 16-22 How to Cite?
AbstractWe present a Temporal Exemplar-based Bayesian Networks (TEBNs) far facial expression recognition. The proposed Bayesian Networks (BNs) consists of three layers: Observation layer, Exemplars layer and Prior Knowledge layer. In the Exemplars layer, exemplar-based model is integrated with BNs to improve the accuracy of probability estimation. In the Prior Knowledge layer, static BNs is extended to Temporal BNs by considering historical observations to model temporal behavior of facial expression. Experiment on CMU expression database illustrates that the proposed TEBNs is very efficient in modeling the evolution of facial deformation. © 2008 IEEE.
DescriptionProceedings of the International Conference on Machine Learning and Applications, 2008, p. 16-22
Persistent Identifierhttp://hdl.handle.net/10722/61208
ISBN
References

 

DC FieldValueLanguage
dc.contributor.authorShang, Len_HK
dc.contributor.authorChan, KPen_HK
dc.date.accessioned2010-07-13T03:33:11Z-
dc.date.available2010-07-13T03:33:11Z-
dc.date.issued2008en_HK
dc.identifier.citationProceedings - 7Th International Conference On Machine Learning And Applications, Icmla 2008, 2008, p. 16-22en_HK
dc.identifier.isbn978-0-7695-3495-4en_HK
dc.identifier.urihttp://hdl.handle.net/10722/61208-
dc.descriptionProceedings of the International Conference on Machine Learning and Applications, 2008, p. 16-22en_HK
dc.description.abstractWe present a Temporal Exemplar-based Bayesian Networks (TEBNs) far facial expression recognition. The proposed Bayesian Networks (BNs) consists of three layers: Observation layer, Exemplars layer and Prior Knowledge layer. In the Exemplars layer, exemplar-based model is integrated with BNs to improve the accuracy of probability estimation. In the Prior Knowledge layer, static BNs is extended to Temporal BNs by considering historical observations to model temporal behavior of facial expression. Experiment on CMU expression database illustrates that the proposed TEBNs is very efficient in modeling the evolution of facial deformation. © 2008 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofProceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008en_HK
dc.rightsInternational Conference on Machine Learning and Applications Proceedings. Copyright © IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2008 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.subjectDistributed parameter networks-
dc.subjectFace recognition-
dc.subjectGesture recognition-
dc.subjectInference engines-
dc.subjectIntelligent networks-
dc.titleTemporal Exemplar-based Bayesian Networks for facial expression recognitionen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=978-0-7695-3495-4 &volume=&spage=16&epage=22&date=2008&atitle=Temporal+Exemplar-based+Bayesian+Networks+for+facial+expression+recognitionen_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/ICMLA.2008.9en_HK
dc.identifier.scopuseid_2-s2.0-60649118559en_HK
dc.identifier.hkuros161859en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-60649118559&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage16en_HK
dc.identifier.epage22en_HK
dc.identifier.scopusauthoridShang, L=55145022200en_HK
dc.identifier.scopusauthoridChan, KP=7406032820en_HK

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