Enhancing facial recognition accuracy and efficiency through integrated CNN, PCA, and SVM techniques

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dc.contributor.authorKashkool, Hawraa Jaafar Murad
dc.contributor.authorFarhan, Hameed Mutlag
dc.contributor.authorNaseri, Raghda Awad Shaban
dc.contributor.authorKurnaz, Sefer
dc.date.accessioned2024-07-19T10:10:38Z
dc.date.available2024-07-19T10:10:38Z
dc.date.issued2024en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractFacial recognition, as a paradigmatic instance of biometric identification, has witnessed escalating utilization across diverse domains, encompassing security, surveillance, human-computer interaction, and personalized user experiences. The fundamental premise underlying this technology resides in its capacity to extract discriminative features from facial images and subsequently classify them accurately. However, the precision and efficiency of such systems remain subject to an array of intricate challenges, necessitating innovative solutions. The overarching aim of this thesis is to enhance the performance and efficacy of facial recognition systems through the seamless integration of Convolutional Neural Networks (CNNs) for feature extraction, Principal Component Analysis (PCA) for dimensionality reduction, and Support Vector Machines (SVMs) for classification. This holistic approach seeks to optimize the accuracy, efficiency, and robustness of facial recognition, thereby contributing to the advancement of computer vision and biometric identification technologies.en_US
dc.identifier.citationKashkool, H. J. M., Farhan, H. M., Naseri, R. A. S., Kurnaz, S. (2024). Enhancing facial recognition accuracy and efficiency through integrated CNN, PCA, and SVM techniques. Lecture Notes in Networks and Systems, 1035 LNNS, 57-70. 10.1007/978-3-031-62871-9_6en_US
dc.identifier.endpage70en_US
dc.identifier.isbn9783031628702
dc.identifier.scopus2-s2.0-85197756684
dc.identifier.scopusqualityQ4
dc.identifier.startpage57en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4770
dc.identifier.volume1035 LNNSen_US
dc.identifier.wosWOS:001286524700006
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKashkool, Hawraa Jaafar Murad
dc.institutionauthorFarhan, Hameed Mutlag
dc.institutionauthorKurnaz, Sefer
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes in Networks and Systems
dc.relation.isversionof10.1007/978-3-031-62871-9_6en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial intelligenceen_US
dc.subjectDimensionality reductionen_US
dc.subjectFace recognitionen_US
dc.subjectFeature extractionen_US
dc.subjectMachine learningen_US
dc.titleEnhancing facial recognition accuracy and efficiency through integrated CNN, PCA, and SVM techniques
dc.typeConference Object

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