A Comparison of deep Learning Based Architecture With a Conventional Approach for Face Recognition Problem

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dc.contributor.authorÜnal, Fatime Zehra
dc.contributor.departmentBilgisayar Mühendisliğitr_TR
dc.contributor.facultyFen Bilimleri Enstitüsütr_TR
dc.date.accessioned2021-11-30T12:18:18Z
dc.date.available2021-11-30T12:18:18Z
dc.date.issued2019-12-01
dc.description.abstractThis paper addresses a new approach for face recognition problem based on deep learning strategy. In order to verify the performance of the proposed approach, it is compared with a conventional face recognition method by using various comprehensive datasets. The conventional approach employs Histogram of Gradient (HOG) algorithm to extract features and utilizes a multi-class Support Vector Machine (SVM) classifier to train and learn the classification. On the other hand, the proposed deep learning based approaches employ a Convolutional Neural Network (CNN) based architecture and also offer both a SVM and Softmax classifiers respectively for the classification phase. Results reveal that the proposed deep learning architecture using Softmax classifier outperform conventional method by a substantial margin. As well as, the deep learning architecture using Softmax classifier also outperform SVM in almost all cases.tr_TR
dc.description.indexTrdizintr_TR
dc.identifier.endpage149tr_TR
dc.identifier.issn/e-issn2618-6462
dc.identifier.issue2tr_TR
dc.identifier.startpage129tr_TR
dc.identifier.urihttp://hdl.handle.net/20.500.12575/76526
dc.identifier.volume69tr_TR
dc.language.isoentr_TR
dc.publisherAnkara Üniversitesi Fen Fakültesitr_TR
dc.relation.journalCommunications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineeringtr_TR
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıtr_TR
dc.subjectConvolutional Neural Networktr_TR
dc.subjectDeep Learningtr_TR
dc.subjectFace Recognitiontr_TR
dc.titleA Comparison of deep Learning Based Architecture With a Conventional Approach for Face Recognition Problemtr_TR
dc.typeArticletr_TR

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