Gender classification from facial images using gray relational analysis with novel local binary pattern descriptors

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dc.authorid0000-0001-5167-1101en_US
dc.authorid0000-0003-0710-0867en_US
dc.contributor.authorKaya, Yılmaz
dc.contributor.authorErtuğrul, Ömer Faruk
dc.date.accessioned2019-07-04T13:13:01Z
dc.date.available2019-07-04T13:13:01Z
dc.date.issued2016-11-18en_US
dc.departmentBatman Üniversitesi Mühendislik - Mimarlık Fakültesi Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractGender classification (GC) is one of the major tasks in human identification that increase its accuracy. Local binary pattern (LBP) is a texture method that employed successfully. But LBP suffers a major problem; it cannot capture spatial relationships among local textures. Therefore, in order to increase the accuracy of GC, two LBP descriptors, which are based on (1) spatial relations between neighbors with a distance parameter, and (2) spatial relations between a reference pixel and its neighbor on the same orientation, were employed to extract features from facial images. Additionally, gray relational analysis (GRA) was carried out to identify gender through extracted features. Experiments on the FEI database illustrated the effectiveness of the proposed approaches. Achieved accuracies are 97.14, 93.33, and 92.50% by applying GRA with the nLBPd, dLBPα, and traditional LBP features, respectively. Experimental results indicated that the proposed approaches were very competitive feature extraction methods in GC. Present work also showed that the nLBPd, dLBPα methods were obtained more acceptable results than traditional LBP.en_US
dc.identifier.citationKaya, Y., Ertuğrul, Ö F. (2016). Gender classification from facial images using gray relational analysis with novel local binary pattern descriptors. Signal, Image and Video Processing, 11(4), pp. 769-776. https://doi.org/10.1007/s11760-016-1021-3en_US
dc.identifier.endpage776en_US
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage769en_US
dc.identifier.urihttps://doi.org/10.1007/s11760-016-1021-3
dc.identifier.urihttps://hdl.handle.net/20.500.12402/2189
dc.identifier.volume11en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.relation.isversionof10.1007/s11760-016-1021-3en_US
dc.relation.journalSignal, Image and Video Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectdLBPαen_US
dc.subjectGender Classificationen_US
dc.subjectGray Relational Analysisen_US
dc.subjectLocal Binary Patternsen_US
dc.subjectnLBPden_US
dc.titleGender classification from facial images using gray relational analysis with novel local binary pattern descriptorsen_US
dc.typeArticleen_US

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