APPROACH TO DETECTION OF FACE OCCLUSION IN ACCESS CONTROL SYSTEMS

dc.contributor.author	Abdukadirov Bakhtiyor
dc.contributor.author	Abdukadirova Gulbahor
dc.date.accessioned	2025-12-29T09:28:52Z
dc.date.issued	2023-02-14
dc.description.abstract	The article analyzes methods for detecting face masks in access control and management systems, and also presents a method of convolution neural networks for detecting face masks using deep machine learning technology. Face mask images in the form of a neural network model were trained on the generated database, and performance metrics were determined using metrics such as model accuracy, F1-score, precision and recall.
dc.format	application/pdf
dc.identifier.uri	https://americanjournal.org/index.php/ajper/article/view/416
dc.identifier.uri	https://asianeducationindex.com/handle/123456789/15213
dc.language.iso	eng
dc.publisher	American Journals
dc.relation	https://americanjournal.org/index.php/ajper/article/view/416/369
dc.rights	https://creativecommons.org/licenses/by-nc/4.0
dc.source	American Journal of Pedagogical and Educational Research; Vol. 9 (2023); 44-48
dc.source	2832-9791
dc.subject	access control system, biometric system, machine learning, deep learning, neural networks, evaluation metrics.
dc.title	APPROACH TO DETECTION OF FACE OCCLUSION IN ACCESS CONTROL SYSTEMS
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	Peer-reviewed Article

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