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dc.contributor.authorKoç, Mehmet
dc.contributor.authorBarkana, Atalay
dc.date.accessioned2019-10-21T20:41:13Z
dc.date.available2019-10-21T20:41:13Z
dc.date.issued2014
dc.identifier.isbn9781479948741
dc.identifier.urihttps://dx.doi.org/10.1109/SIU.2014.6830283
dc.identifier.urihttps://hdl.handle.net/11421/20706
dc.description2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 -- 23 April 2014 through 25 April 2014 -- Trabzon -- 106053en_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 databaseen_US
dc.language.isoturen_US
dc.publisherIEEE Computer Societyen_US
dc.relation.isversionof10.1109/SIU.2014.6830283en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCommon Vector Approachen_US
dc.subjectFace Recognitionen_US
dc.subjectOcclusionen_US
dc.titleModular common vector approach [Modüler ortak vektör yaklaşimi]en_US
dc.typeconferenceObjecten_US
dc.relation.journal2014 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedingsen_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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