Robust face recognition algorithm based on linear operators discrete wavelet transformation and simple linear regression

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dc.contributor.authorAlazzawi, Abdulbasit
dc.contributor.authorUçan, Osman Nuri
dc.contributor.authorBayat, Oğuz
dc.date.accessioned2021-05-15T12:42:12Z
dc.date.available2021-05-15T12:42:12Z
dc.date.issued2018
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Elektrik - Elektronik Mühendisliği Bölümüen_US
dc.description1rst International Conference on Data Science, E-Learning and Information Systems (DATA) -- OCT 01-02, 2018 -- Univ Distancia Madrid, Madrid, SPAIN
dc.descriptionalazzawi, abdulbasit/0000-0001-9210-7080
dc.description.abstractFace recognition system performance would sharply decrease if there were noticeable issues in the face images conditions such as light variation, contrast, and brightness issues that can deeply affect the system performance directly. The process of face analysis comes here to put the face image environment in a spot of light to enable the interested researchers to find out the factors that have vital effects on these systems. In this paper, we are producing a hybrid method that based on discrete wavelet transformation DWT output and linear edge detection operators such as Sobel, Prewitt and Roberts output as a solution to cover some of these related image condition issues. For feature extraction, a new method based on simple linear regression slope with -SLP name-that proved the ability to find features in critical regions of the face, and eigenface based on principal components analysis PCA used with linear edge detection operators for comparison, studying the interrelation among them, and investigation the effects on the performance of the proposed system. A segmentation used to handle the details of a face image by dividing dataset images to equaled size blocks. Modified Artificial neural network MANN used for classification and all results obtained evaluated. The proposed method examined and evaluated with different face datasets using modified MANN classifier. The experimental results were displaying the superiority of the proposed algorithm over the algorithms that used the state-of-art techniques.en_US
dc.description.sponsorshipInt Assoc Researchersen_US
dc.identifier.doi10.1145/3279996.3279997
dc.identifier.isbn978-1-4503-6536-9
dc.identifier.scopus2-s2.0-85058183817
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1145/3279996.3279997
dc.identifier.urihttps://hdl.handle.net/20.500.12939/908
dc.identifier.wosWOS:000511409000001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorUçan, Osman Nuri
dc.institutionauthorBayat, Oğuz
dc.institutionauthorAlazzawi, Abdulbasit
dc.language.isoen
dc.publisherAssoc Computing Machineryen_US
dc.relation.ispartofProceedings of the First International Conference on Data Science, E-Learning and Information Systems 2018 (Data'18)
dc.relation.ispartofseriesACM International Conference Proceedings Series
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFace Analysisen_US
dc.subjectFace Recognitionen_US
dc.subjectNetworken_US
dc.subjectPCA SLPen_US
dc.subjectMANNen_US
dc.titleRobust face recognition algorithm based on linear operators discrete wavelet transformation and simple linear regression
dc.typeConference Object

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WoS İndeksli Yayınlar Koleksiyonu
Elektrik - Elektronik Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu