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dc.contributor.authorHajati, Farshiden_US
dc.contributor.authorLucas, Caroen_US
dc.contributor.authorGao, Yongshengen_US
dc.contributor.editorJian Zhang, Chunhua Shen, Glenn Geers, Qiang Wuen_US
dc.date.accessioned2017-04-24T10:00:29Z
dc.date.available2017-04-24T10:00:29Z
dc.date.issued2010en_US
dc.date.modified2011-06-02T05:07:31Z
dc.identifier.refurihttp://dicta2010.conference.nicta.com.au/en_AU
dc.identifier.doi10.1109/DICTA.2010.116en_AU
dc.identifier.urihttp://hdl.handle.net/10072/39002
dc.description.abstractIn this paper, a new method for face localization in color images, which is based on co-evolutionary systems, is introduced. The proposed method uses a co-evolutionary system to locate the eyes in a face image. The used coevolutionary system involves two genetic algorithm models. The first GA model searches for a solution in the given environment, and the second GA model searches for useful genetic information in the first GA model. In the next step, by using the location of eyes in image the parameters of face's bounding ellipse (center, orientation, major and minor axis) are computed. To evaluate and compare the proposed method with other methods, high order Pseudo Zernike Moments (PZM) are utilized to produce feature vectors and a Radial Basis Function (RBF) neural network is used as the classifier. Simulation results indicate that the speed and accuracy of the new system using the proposed face localization method which uses a co-evolutionary approach is higher than the system proposed in [10]. Keywords-face localization; genetic algorithm; coevolutionaryen_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent605429 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherIEEEen_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencename2010 Digital Image Computing: Techniques and Applications (DICTA 2010)en_US
dc.relation.ispartofconferencetitleProceedings 2010 Digital Image Computing: Techniques and Applications DICTA 2010en_US
dc.relation.ispartofdatefrom2010-12-01en_US
dc.relation.ispartofdateto2010-12-03en_US
dc.relation.ispartoflocationSydney, Australiaen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280208en_US
dc.subject.fieldofresearchcode280207en_US
dc.titleFace Localization using an Effective Co-Evolutionary Genetic Algorithmen_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. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_AU
gro.date.issued2010
gro.hasfulltextFull Text


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