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dc.contributor.authorZhang, B
dc.contributor.authorGao, Y
dc.contributor.authorQiao, Y
dc.contributor.editorGang Qian
dc.date.accessioned2017-05-03T14:13:02Z
dc.date.available2017-05-03T14:13:02Z
dc.date.issued2008
dc.date.modified2010-07-06T06:59:58Z
dc.identifier.isbn9781424417643
dc.identifier.issn1522-4880
dc.identifier.refurihttp://www.icip08.org/default.asp
dc.identifier.doi10.1109/ICIP.2008.4712152
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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent492617 bytes
dc.format.extent566 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.languageEnglish
dc.language.isoeng
dc.publisherIEEE
dc.publisher.placeUSA
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofconferencenameThe IEEE International Conference on Image Processing (ICIP) 2008
dc.relation.ispartofconferencetitleProceedings - International Conference on Image Processing, ICIP
dc.relation.ispartofdatefrom2008-10-12
dc.relation.ispartofdateto2008-10-15
dc.relation.ispartoflocationSan Diego, California, U.S.A
dc.relation.ispartofpagefrom1904
dc.relation.ispartofpageto1907
dc.rights.retentionY
dc.subject.fieldofresearchcode280208
dc.subject.fieldofresearchcode280207
dc.titleFace Recognition based on Gradient Gabor feature
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.rights.copyright© 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.
gro.date.issued2008
gro.hasfulltextFull Text
gro.griffith.authorGao, Yongsheng


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

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