%A Dong Dong Nguyen
%A Ngoc Dung Bui
%A Xuan Tung Hoang
%A Nguyen Cac Tran
%T POSE INVARIANT FACE RECOGNITION SYSTEM
%X Face recognition has become popular in security surveillance systems. These systems work well only when the face is in frontal and the system itself has considerable training data. This paper proposes a method of face recognition with various view using Generative Adversarial Networks (GAN). In this method, a pre-trained model of face is used to generate the training data of different pose variations from several frontal faces using GAN. These faces will be used as input of the FaceNet and Support Vector Machine to be trained to perform the task of face recognition. The experiment results demonstrate the accuracy of the proposed method in the case of lacking the training data. This paper focus on the applicability of Generative Adversarial Network in Face Recognition System by applying the GAN-based method in Face Recognition System to increase the number of training samples and detect the faces of different poses that was not possible with the conventional Face Recognition System. The comparison between Face Recognition System using GAN and conventional Face Recognition System was presented.
%D 2020
%P 33-40
%L SisLab4277