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dc.contributor.authorKoç, Mehmet
dc.contributor.authorBarkana, Atalay
dc.date.accessioned2019-10-21T20:11:42Z
dc.date.available2019-10-21T20:11:42Z
dc.date.issued2014
dc.identifier.isbn978-1-4799-4874-1
dc.identifier.issn2165-0608
dc.identifier.urihttps://hdl.handle.net/11421/20300
dc.description22nd IEEE Signal Processing and Communications Applications Conference (SIU) -- APR 23-25, 2014 -- Karadeniz Teknik Univ, Trabzon, TURKEYen_US
dc.descriptionWOS: 000356351400112en_US
dc.description.abstractThe performance of a face recognition system is negatively affected by the accessories used on the face Like many methods, the recognition performance of the Common Vector Approach (CVA) [1] over occluded images is not at the desired level. In this work, we proposed an extension of the CVA, namely the Modular Common Vector Approach (M-CVA), which improves the recognition performance at the occluded face images. M-CVA outperforms CVA by a margin of 82,7 percent in the experiments which are conducted over AR face database.en_US
dc.description.sponsorshipIEEE, Karadeniz Tech Univ, Dept Comp Engn & Elect & Elect Engnen_US
dc.language.isoturen_US
dc.publisherIEEEen_US
dc.relation.ispartofseriesSignal Processing and Communications Applications Conference
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCommon Vector Approachen_US
dc.subjectFace Recognitionen_US
dc.subjectOcclusionen_US
dc.titleModular Common Vector Approachen_US
dc.typeconferenceObjecten_US
dc.relation.journal2014 22Nd Signal Processing and Communications Applications Conference (Siu)en_US
dc.contributor.departmentAnadolu Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümüen_US
dc.identifier.startpage533en_US
dc.identifier.endpage535en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.contributor.institutionauthorBarkana, Atalay


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