eprintid: 4277
rev_number: 9
eprint_status: archive
userid: 402
dir: disk0/00/00/42/77
datestamp: 2020-12-16 09:45:31
lastmod: 2020-12-16 09:45:31
status_changed: 2020-12-16 09:45:31
type: conference_item
metadata_visibility: show
creators_name: Nguyen, Dong Dong
creators_name: Bui, Ngoc Dung
creators_name: Hoang, Xuan Tung
creators_name: Tran, Nguyen Cac
creators_id: dnbui@utc.edu.vn
creators_id: tunghx@vnu.edu.vn
title: POSE INVARIANT FACE RECOGNITION SYSTEM
ispublished: pub
subjects: IT
divisions: fac_fit
abstract: 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.
date: 2020-12-11
date_type: published
full_text_status: public
pres_type: paper
pagerange: 33-40
event_title: INTERNATIONAL CONFERENCE ON APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN TRANSPORTATION
event_type: conference
refereed: FALSE
citation:   Nguyen, Dong Dong and Bui, Ngoc Dung and Hoang, Xuan Tung and Tran, Nguyen Cac  (2020) POSE INVARIANT FACE RECOGNITION SYSTEM.  In: INTERNATIONAL CONFERENCE ON APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN TRANSPORTATION.     
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4277/1/AITC2020%20Proceedings%20paper.pdf