Gender Classification from Face Images by Using Local Binary Pattern and Gray-Level Co-Occurrence Matrix

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dc.contributor.authorUzbas, Betul
dc.contributor.authorArslan, Ahmet
dc.date.accessioned2020-03-26T19:54:03Z
dc.date.available2020-03-26T19:54:03Z
dc.date.issued2018
dc.departmentSelçuk Üniversitesien_US
dc.description10th International Conference on Machine Vision (ICMV) -- NOV 13-15, 2017 -- Vienna, AUSTRIAen_US
dc.description.abstractGender is an important step for human computer interactive processes and identification. Human face image is one of the important sources to determine gender. In the present study, gender classification is performed automatically from facial images. In order to classify gender, we propose a combination of features that have been extracted face, eye and lip regions by using a hybrid method of Local Binary Pattern and Gray-Level Co-Occurrence Matrix. The features have been extracted from automatically obtained face, eye and lip regions. All of the extracted features have been combined and given as input parameters to classification methods (Support Vector Machine, Artificial Neural Networks, Naive Bayes and k-Nearest Neighbor methods) for gender classification. The Nottingham Scan face database that consists of the frontal face images of 100 people (50 male and 50 female) is used for this purpose. As the result of the experimental studies, the highest success rate has been achieved as 98% by using Support Vector Machine. The experimental results illustrate the efficacy of our proposed method.en_US
dc.description.sponsorshipSelcuk University Scientific Research Projects FundSelcuk University [17701252]; TUBITAK (The Scientific and Technological Research Council of Turkey)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)en_US
dc.description.sponsorshipThe authors acknowledge the support of this study provided by Selcuk University Scientific Research Projects Fund for project no: 17701252. Also, the authors would like to thank TUBITAK (The Scientific and Technological Research Council of Turkey) for 2211 scholarship.en_US
dc.identifier.doi10.1117/12.2309771en_US
dc.identifier.isbn978-1-5106-1942-5
dc.identifier.issn0277-786Xen_US
dc.identifier.issn1996-756Xen_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://dx.doi.org/10.1117/12.2309771
dc.identifier.urihttps://hdl.handle.net/20.500.12395/36652
dc.identifier.volume10696en_US
dc.identifier.wosWOS:000432481200033en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPIE-INT SOC OPTICAL ENGINEERINGen_US
dc.relation.ispartofTENTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2017)en_US
dc.relation.ispartofseriesProceedings of SPIE
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectGender classificationen_US
dc.subjectgray-level co-occurrence matrixen_US
dc.subjectlocal binary patternen_US
dc.subjectimage processingen_US
dc.titleGender Classification from Face Images by Using Local Binary Pattern and Gray-Level Co-Occurrence Matrixen_US
dc.typeConference Objecten_US

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