Performance of face recognition system using gradient laplacian operators and new features extraction method based on linear regression slope

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dc.contributor.authorAlazzawi, Abdulbasit
dc.contributor.authorUçan, Osman Nuri
dc.contributor.authorBayat, Oğuz
dc.date.accessioned2021-05-15T12:42:07Z
dc.date.available2021-05-15T12:42:07Z
dc.date.issued2018
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionalazzawi, abdulbasit/0000-0001-9210-7080
dc.description.abstractRecent research proves that face recognition systems can achieve high-quality results even in non-ideal environments. Edge detection techniques and feature extraction methods are popular mechanisms used in face recognition systems. Edge detection can be used to construct the face map in the image efficiently, in which feature extraction techniques generate the most suitable features that can identify human faces. In this study, we present a new and efficient face recognition system that uses various gradient- and Laplacian-based operators with a new feature extraction method. Different edge detection operators are exploited to obtain the best image edges. The new and robust method based on the slope of the linear regression, called SLP, uses the estimated face lines in its feature extraction step. Artificial neural network (ANN) is used as a classifier. To determine the best scheme that gives the best performance, we test combinations of various techniques such as (Sobel filter (SF), SLP/principal component analysis (PCA), ANN), (Prewitt filter(PF), SLP/PCA, ANN), (Roberts filter (RF), SLP/PCA, ANN), (zero cross filter (ZF), SLP/PCA, ANN), (Laplacian of Gaussian filter (LG), SLP/PCA, ANN), and (Canny filter(CF), SLP/PCA, ANN). The BIO ID dataset is used in the training and testing phases for the proposed face recognition system combinations. Experimental results indicate that the proposed schemes achieve satisfactory results with high-accuracy classification. Notably, the combinations of (SF, SLP, ANN) and (ZF, SLP, ANN) gain the best results and outperform all the other algorithm combinations.en_US
dc.identifier.doi10.1155/2018/1929836
dc.identifier.issn1024-123X
dc.identifier.issn1563-5147
dc.identifier.scopus2-s2.0-85056495043
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1155/2018/1929836
dc.identifier.urihttps://hdl.handle.net/20.500.12939/897
dc.identifier.volume2018en_US
dc.identifier.wosWOS:000446692500001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBayat, Oğuz
dc.institutionauthorUçan, Osman Nuri
dc.institutionauthorAlazzawi, Abdulbasit
dc.language.isoen
dc.publisherHindawi Ltden_US
dc.relation.ispartofMathematical Problems in Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEdge Detection Algorithmsen_US
dc.subjectFace Recognitionen_US
dc.titlePerformance of face recognition system using gradient laplacian operators and new features extraction method based on linear regression slope
dc.typeArticle

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