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dc.contributor.authorZhao, Sanqiangen_US
dc.contributor.authorGao, Yongshengen_US
dc.contributor.authorZhang, Baochangen_US
dc.contributor.editorT.K. Ho; G. Sanniti di Bajaen_US
dc.date.accessioned2017-04-24T12:26:41Z
dc.date.available2017-04-24T12:26:41Z
dc.date.issued2009en_US
dc.date.modified2010-06-18T03:53:10Z
dc.identifier.issn01678655en_US
dc.identifier.doi10.1016/j.patrec.2009.03.007en_AU
dc.identifier.urihttp://hdl.handle.net/10072/28503
dc.description.abstractFeature extraction and classification using Gabor wavelets have proven to be successful in computer vision and pattern recognition. Gabor feature-based Elastic Bunch Graph Matching (EBGM), which demonstrated excellent performance in the FERET evaluation test, has been considered as one of the best algorithms for face recognition due to its robustness against expression, illumination and pose variations. However, EBGM involves considerable computational complexity in its rigid and deformable matching process, preventing its use in many real-time applications. This paper presents a new Constrained Profile Model (CPM), in cooperation with Flexible Shape Model (FSM) to form an efficient localization framework. Through Gabor feature constrained local alignment, the proposed method not only avoids local minima in landmark localization, but also circumvents the exhaustive global optimization. Experiments on CAS-PEAL and FERET databases demonstrated the effectiveness and efficiency of the proposed method.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent595459 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherElsevier B.V.en_US
dc.publisher.placeNetherlandsen_US
dc.relation.ispartofstudentpublicationYen_AU
dc.relation.ispartofpagefrom922en_US
dc.relation.ispartofpageto930en_US
dc.relation.ispartofissue10en_US
dc.relation.ispartofjournalPattern Recognition Lettersen_US
dc.relation.ispartofvolume30en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280207en_US
dc.subject.fieldofresearchcode280208en_US
dc.titleGabor Feature Constrained Statistical Model for Efficient Landmark Localization and Face Recognitionen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.rights.copyrightCopyright 2009 Elsevier B.V. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.en_AU
gro.date.issued2009
gro.hasfulltextFull Text


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