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dc.contributor.authorZhang, Billen_US
dc.contributor.authorGao, Yongshengen_US
dc.contributor.authorQiao, Yuen_US
dc.contributor.editorGang Qianen_US
dc.date.accessioned2017-04-24T11:28:13Z
dc.date.available2017-04-24T11:28:13Z
dc.date.issued2008en_US
dc.date.modified2010-07-06T06:59:58Z
dc.identifier.refurihttp://www.icip08.org/default.aspen_AU
dc.identifier.doi10.1109/ICIP.2008.4712152en_AU
dc.identifier.urihttp://hdl.handle.net/10072/22702
dc.description.abstractIn this paper, a novel Gradient Gabor (GGabor) filter is proposed to extract multi-scale and multi-orientation features to represent and classify faces. Gradient Gabor combines the derivative of Gaussian functions and the harmonic functions to capture the features in both spatial and frequency domains to deliver orientation and scale information. The spatial positions are combined into Gaussian derivatives which allows it to provide more stable information. An Efficient Kernel Fisher analysis method is proposed to find multiple subspaces based on both GGabor magnitude and phase features, which is a local kernel mapping method to capture the structure information in faces. Experiments on two face databases, FRGC Version 1 and FRGC Version 2, are conducted to compare the performances of the Gabor and GGabor features, which show that GGabor can also be a powerful tool to model faces, and the Efficient Kernel Fisher classifier can improve the efficiency of the original kernel fisher method.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent492617 bytes
dc.format.extent566 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherIEEEen_US
dc.publisher.placeUSAen_US
dc.relation.ispartofstudentpublicationYen_AU
dc.relation.ispartofconferencenameThe IEEE International Conference on Image Processing (ICIP) 2008en_US
dc.relation.ispartofconferencetitleProceedings of the 2008 IEEE International Conference on Image Processing (ICIP)en_US
dc.relation.ispartofdatefrom2008-10-12en_US
dc.relation.ispartofdateto2008-10-15en_US
dc.relation.ispartoflocationSan Diego, California, U.S.Aen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280208en_US
dc.subject.fieldofresearchcode280207en_US
dc.titleFace Recognition based on Gradient Gabor featureen_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 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.en_AU
gro.date.issued2008
gro.hasfulltextFull Text


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

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