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Artificial and Deceitful Faces Detection Using Machine Learning

By
Balusamy Nachiappan ,
Balusamy Nachiappan

Prologis, Denver, Colorado 80202 USA

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N Rajkumar ,
N Rajkumar

Department of Computer Science & Engineering, Alliance College of Engineering and Design, Alliance University, Bangalore, Karnataka, India

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C Viji ,
C Viji

Department of Computer Science & Engineering, Alliance College of Engineering and Design, Alliance University, Bangalore, Karnataka, India

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Mohanraj A ,
Mohanraj A

Sri Eshwar College of Engineering, Coimbatore, Tamilnadu, India

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Abstract

Security 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 (CNN) 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.

How to Cite

1.
Nachiappan B, Rajkumar N, Viji C, A M. Artificial and Deceitful Faces Detection Using Machine Learning. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 Mar. 11 [cited 2024 May 21];3:611. Available from: https://conferencias.saludcyt.ar/index.php/sctconf/article/view/611

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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