Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/15673
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dc.contributor.authorNachiappan, Balusamy-
dc.contributor.authorRajkumar, N-
dc.contributor.authorViji, C-
dc.contributor.authorMohanraj, A-
dc.date.accessioned2024-05-29T08:53:01Z-
dc.date.available2024-05-29T08:53:01Z-
dc.date.issued2024-
dc.identifier.citationVol. 3en_US
dc.identifier.issn2953-4860-
dc.identifier.urihttp://dx.doi.org/10.56294/sctconf2024611-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/15673-
dc.description.abstractSecurity certification is becoming popular for many applications, such as significant financial transactions. PIN and password authentication is the most common method of authentication. Due to the finite length of the password, the security level is low and can be easily damaged. Adding a new dimension to the sensing mode-driven state-of-the-art multi-modal boundary face recognition system of the image-based solutions. It combines the active complex visual features extracted from the latest facial recognition model and uses a custom Convolution Neural Network issue facial authentications and extraction capabilities to ensure the safety of face recognition. The Echo function is dependent on the geometry and material of the face, not disguised by the pictures and videos, such as multi-modal design is easy to image-based face recognition system. © 2024; Los autores.en_US
dc.language.isoenen_US
dc.publisherSalud, Ciencia y Tecnologia - Serie de Conferenciasen_US
dc.publisherEditorial Salud, Ciencia y Tecnologiaen_US
dc.subjectConvolution Neural Networken_US
dc.subjectDeep Learningen_US
dc.subjectGenerative Adversarial Networken_US
dc.subjectImage Forensicsen_US
dc.titleArtificial and Deceitful Faces Detection Using Machine Learning; [Detección De Rostros Artificiales Y Engañosos Mediante Aprendizaje Automático]en_US
dc.typeArticleen_US
Appears in Collections:Journal Articles

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