An algorithm of face recognition based on generative adversarial networks

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.