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dc.contributor.authorZhao, Sanqiangen_US
dc.contributor.authorGao, Yongshengen_US
dc.contributor.authorZhang, Baochangen_US
dc.contributor.editorIAPRen_US
dc.date.accessioned2017-04-24T12:26:44Z
dc.date.available2017-04-24T12:26:44Z
dc.date.issued2010en_US
dc.date.modified2011-04-18T06:54:32Z
dc.identifier.refurihttp://www.icpr2010.org/en_AU
dc.identifier.doi10.1109/ICPR.2010.317en_AU
dc.identifier.urihttp://hdl.handle.net/10072/36141
dc.description.abstractFace recognition using micropattern representation has recently received much attention in the computer vision and pattern recognition community. Previous researches demonstrated that micropattern representation based on Gabor features achieves better performance than its direct usage on gray-level images. This paper conducts a comparative performance evaluation of micropattern representations on four forms of Gabor features for face recognition. Three evaluation rules are proposed and observed for a fair comparison. To reduce the high feature dimensionality problem, uniform quantization is used to partition the spatial histograms. The experimental results reveal that: 1) micropattern representation based on Gabor magnitude features outperforms the other three representations, and the performances of the other three are comparable; and 2) micropattern representation based on the combination of Gabor magnitude and phase features performs the best.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent556939 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherIEEE Computer Societyen_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencenameThe 20th International Conference on Pattern Recognition (ICPR 2010)en_US
dc.relation.ispartofconferencetitleProceedings of the 20th International Conference on Pattern Recognition (ICPR 2010)en_US
dc.relation.ispartofdatefrom2010-08-23en_US
dc.relation.ispartofdateto2010-08-26en_US
dc.relation.ispartoflocationIstanbul, Turkeyen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchPattern Recognition and Data Miningen_US
dc.subject.fieldofresearchComputer Visionen_US
dc.subject.fieldofresearchcode080109en_US
dc.subject.fieldofresearchcode080104en_US
dc.titlePerformance Evaluation of Micropattern Representation on Gabor Features for Face Recognitionen_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 2010 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.issued2010
gro.hasfulltextFull Text


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

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