Face Recognition as a Method of Authentication in a Web-Based System

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dc.contributor.authorMugalu, Ben Wycliff
dc.contributor.authorWamala, Rodrick Calvin
dc.contributor.authorSerugunda, Jonathan
dc.contributor.authorKatumba, Andrew
dc.date.accessioned2022-11-29T20:00:38Z
dc.date.available2022-11-29T20:00:38Z
dc.date.issued2021
dc.description.abstractOnline information systems currently heavily rely on the username and password traditional method for protecting information and controlling access. With the advancement in biometric technology and popularity of fields like AI and Machine Learning, biometric security is becoming increasingly popular because of the usability advantage. This paper reports how machine learning based face recognition can be integrated into a web-based system as a method of authentication to reap the benefits of improved usability. This paper includes a comparison of combinations of detection and classification algorithms with FaceNet for face recognition. The results show that a combination of MTCNN for detection, Facenet for generating embeddings, and LinearSVC for classification out performs other combinations with a 95% accuracy. The resulting classifier is integrated into the web-based system and used for authenticating users.en_US
dc.identifier.citationMugalu, B. W., Wamala, R. C., Serugunda, J., & Katumba, A. (2021). Face Recognition as a Method of Authentication in a Web-Based System. arXiv preprint arXiv:2103.15144.en_US
dc.identifier.urihttps://arxiv.org/abs/2103.15144
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/5536
dc.language.isoenen_US
dc.publisherarXiv preprint arXiven_US
dc.subjectFaceNeten_US
dc.subjectMTCNNen_US
dc.subjectFace Recognitionen_US
dc.subjectMachine Learningen_US
dc.subjectBiometric Authenticationen_US
dc.subjectLinearSVCen_US
dc.titleFace Recognition as a Method of Authentication in a Web-Based Systemen_US
dc.typeArticleen_US
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