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dc.contributor.authorZhao, Sanqiangen_US
dc.contributor.authorGao, Yongshengen_US
dc.contributor.editorIAPRen_US
dc.date.accessioned2017-05-03T14:12:57Z
dc.date.available2017-05-03T14:12:57Z
dc.date.issued2006en_US
dc.date.modified2010-10-27T08:29:35Z
dc.identifier.doi10.1109/ICPR.2006.291en_AU
dc.identifier.urihttp://hdl.handle.net/10072/13121
dc.description.abstractFace pose estimation forms an important part in a face recognition system. However, fully automated and accurate pose determination still remains an unsolved problem in the research community. In this paper, we propose a novel Elastic Energy Model to automatically estimate face poses. Our method employs statistical energy contributions of a set of feature points, which can avoid over-trusting selected anchor points. It provides a robust solution to the feature localisation inaccuracy problem, which is inevitable in practical applications with cluttered backgrounds. As a general configuration, our model can be easily implemented and extended to other non-rigid objects. Its effectiveness and robustness are revealed in our experiments.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent320708 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherIEEE Computer Societyen_US
dc.publisher.placeLos Alamitos, California, USAen_US
dc.relation.ispartofstudentpublicationYen_AU
dc.relation.ispartofconferencenameThe 18th International Conference of Pattern Recognition (ICPR)en_US
dc.relation.ispartofconferencetitleProceedings: The 18th International Conference of Pattern Recognitionen_US
dc.relation.ispartofdatefrom2006-08-20en_US
dc.relation.ispartofdateto2006-08-24en_US
dc.relation.ispartoflocationHong Kongen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280207en_US
dc.titleAutomated Face Pose Estimation Using Elastic Energy Modelsen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.rights.copyrightCopyright 2006 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.en_AU
gro.date.issued2006
gro.hasfulltextFull Text


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    Contains papers delivered by Griffith authors at national and international conferences.

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