An algorithm of face recognition based on generative adversarial networks

Sergey Leonov; Alexander Vasilyev; Artyom Makovetskii; J. Diaz-Escobar

doi:10.1117/12.2321039

17 September 2018 An algorithm of face recognition based on generative adversarial networks

Sergey Leonov, Alexander Vasilyev, Artyom Makovetskii, J. Diaz-Escobar

Proceedings Volume 10752, Applications of Digital Image Processing XLI; 107522L (2018) https://doi.org/10.1117/12.2321039
Event: SPIE Optical Engineering + Applications, 2018, San Diego, California, United States

Abstract

The problem of face recognition is the important task in the security field, closed-circuit television (CCTV), artificial intelligence, and etc. One of the most effective approaches for pattern recognition is the use of artificial neural networks. In this presentation, an algorithm using generative adversarial networks is developed for face recognition. The proposed method consists in the interaction of two neural networks. The first neural network (generative network) generates face patterns, and the second network (discriminative network) rejects false face patterns. Neural network of feed forward type (single-layer or multilayer perceptron) is used as generative network. The convolutional neural network is used as discriminative network for the purpose of pattern selection. A big database of normalized to brightness changes and standardized in scale artificial images is created for the training of neural networks. New facial images are synthesized from existing ones. Results obtained with the proposed algorithm using generative adversarial networks are presented and compared with common algorithms in terms of recognition and classification efficiency and speed of processing.

Citation Download Citation

Sergey Leonov, Alexander Vasilyev, Artyom Makovetskii, and J. Diaz-Escobar "An algorithm of face recognition based on generative adversarial networks", Proc. SPIE 10752, Applications of Digital Image Processing XLI, 107522L (17 September 2018); https://doi.org/10.1117/12.2321039

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available