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The Neural Network Models Effectiveness for Face Detection and Face Recognition

dc.rights.licenseVisos teisės saugomos / All rights reserveden_US
dc.contributor.authorSmelyakov, Kirill
dc.contributor.authorChupryna, Anastasiya
dc.contributor.authorBohomolov, Oleksandr
dc.contributor.authorHunko, Nikita
dc.date.accessioned2025-12-16T10:53:09Z
dc.date.available2025-12-16T10:53:09Z
dc.date.issued2021
dc.identifier.isbn9781665449298en_US
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/159561
dc.description.abstractThe paper is devoted to estimation of the effectiveness of the use of modern convolutional neural networks for face detection and face recognition. On standard and custom datasets, learning of neural networks and comparison of the effectiveness of their functioning are carried out. An algorithm and recommendations are proposed regarding the practical application of the neural networks for detecting faces on digital photographs.en_US
dc.format.extent7 p.en_US
dc.format.mediumTekstas / Texten_US
dc.language.isoenen_US
dc.relation.urihttps://etalpykla.vilniustech.lt/handle/123456789/159397en_US
dc.source.urihttps://ieeexplore.ieee.org/document/9431476en_US
dc.subjectConvolutional Neural Networken_US
dc.subjectFace Detectionen_US
dc.subjectFace Recognitionen_US
dc.subjectModelen_US
dc.subjectEffectivenessen_US
dc.titleThe Neural Network Models Effectiveness for Face Detection and Face Recognitionen_US
dc.typeKonferencijos publikacija / Conference paperen_US
dcterms.accrualMethodRankinis pateikimas / Manual submissionen_US
dcterms.issued2021-05-20
dcterms.references34en_US
dc.description.versionTaip / Yesen_US
dc.contributor.institutionKharkiv National University of Radio Electronicsen_US
dcterms.sourcetitle2021 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), April 22, 2021, Vilnius, Lithuaniaen_US
dc.identifier.eisbn9781665449281en_US
dc.identifier.eissn2690-8506en_US
dc.publisher.nameIEEEen_US
dc.publisher.countryUnited States of Americaen_US
dc.publisher.cityNew Yorken_US
dc.identifier.doihttps://doi.org/10.1109/eStream53087.2021.9431476en_US


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