A SURVEY ON VIDEO FACE RECOGNITION USING DEEP LEARNING; [Tinjauan Berkenaan Pengecaman Wajah Video Menggunakan Pembelajaran Mendalam]

The research on facial recognition consists of Still-Image Face Recognition (SIFR) and Video Face Recognition (VFR), is a common subject being debated among researchers since it does not require any touch like other biometric identification, such as fingerprints and palm prints. Various methods have...

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Published in:Journal of Quality Measurement and Analysis
Main Author: Mustapha M.F.; Mohamad N.M.; Hamid S.H.A.B.; Malik M.A.A.; Noor M.R.M.
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202572556&partnerID=40&md5=dcd433d00452f0a6c58ccee44aa8a778
id 2-s2.0-85202572556
spelling 2-s2.0-85202572556
Mustapha M.F.; Mohamad N.M.; Hamid S.H.A.B.; Malik M.A.A.; Noor M.R.M.
A SURVEY ON VIDEO FACE RECOGNITION USING DEEP LEARNING; [Tinjauan Berkenaan Pengecaman Wajah Video Menggunakan Pembelajaran Mendalam]
2022
Journal of Quality Measurement and Analysis
18
1

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202572556&partnerID=40&md5=dcd433d00452f0a6c58ccee44aa8a778
The research on facial recognition consists of Still-Image Face Recognition (SIFR) and Video Face Recognition (VFR), is a common subject being debated among researchers since it does not require any touch like other biometric identification, such as fingerprints and palm prints. Various methods have been proposed and developed to solve the problems of face recognition. Convolutional Neural Network (CNN) is one of the deep learning techniques that is suggested for both SIFR and VFR. However, several issues related to VFR have still not been solved. Hence, the objective of this paper is to review VFR using deep learning that specifically focuses on several steps of VFR. The VFR steps consists of six main stages; input video of the face, face anti-spoofing module, face and landmark detection, preprocessing, facial feature extraction and face output that include identification or verification result. A summary of implementation of deep learning within VFR steps is discussed. Finally, some directions for future research are also discussed. © 2022, Penerbit Universiti Kebangsaan Malaysia. All rights reserved.
Penerbit Universiti Kebangsaan Malaysia
18235670
English
Article

author Mustapha M.F.; Mohamad N.M.; Hamid S.H.A.B.; Malik M.A.A.; Noor M.R.M.
spellingShingle Mustapha M.F.; Mohamad N.M.; Hamid S.H.A.B.; Malik M.A.A.; Noor M.R.M.
A SURVEY ON VIDEO FACE RECOGNITION USING DEEP LEARNING; [Tinjauan Berkenaan Pengecaman Wajah Video Menggunakan Pembelajaran Mendalam]
author_facet Mustapha M.F.; Mohamad N.M.; Hamid S.H.A.B.; Malik M.A.A.; Noor M.R.M.
author_sort Mustapha M.F.; Mohamad N.M.; Hamid S.H.A.B.; Malik M.A.A.; Noor M.R.M.
title A SURVEY ON VIDEO FACE RECOGNITION USING DEEP LEARNING; [Tinjauan Berkenaan Pengecaman Wajah Video Menggunakan Pembelajaran Mendalam]
title_short A SURVEY ON VIDEO FACE RECOGNITION USING DEEP LEARNING; [Tinjauan Berkenaan Pengecaman Wajah Video Menggunakan Pembelajaran Mendalam]
title_full A SURVEY ON VIDEO FACE RECOGNITION USING DEEP LEARNING; [Tinjauan Berkenaan Pengecaman Wajah Video Menggunakan Pembelajaran Mendalam]
title_fullStr A SURVEY ON VIDEO FACE RECOGNITION USING DEEP LEARNING; [Tinjauan Berkenaan Pengecaman Wajah Video Menggunakan Pembelajaran Mendalam]
title_full_unstemmed A SURVEY ON VIDEO FACE RECOGNITION USING DEEP LEARNING; [Tinjauan Berkenaan Pengecaman Wajah Video Menggunakan Pembelajaran Mendalam]
title_sort A SURVEY ON VIDEO FACE RECOGNITION USING DEEP LEARNING; [Tinjauan Berkenaan Pengecaman Wajah Video Menggunakan Pembelajaran Mendalam]
publishDate 2022
container_title Journal of Quality Measurement and Analysis
container_volume 18
container_issue 1
doi_str_mv
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202572556&partnerID=40&md5=dcd433d00452f0a6c58ccee44aa8a778
description The research on facial recognition consists of Still-Image Face Recognition (SIFR) and Video Face Recognition (VFR), is a common subject being debated among researchers since it does not require any touch like other biometric identification, such as fingerprints and palm prints. Various methods have been proposed and developed to solve the problems of face recognition. Convolutional Neural Network (CNN) is one of the deep learning techniques that is suggested for both SIFR and VFR. However, several issues related to VFR have still not been solved. Hence, the objective of this paper is to review VFR using deep learning that specifically focuses on several steps of VFR. The VFR steps consists of six main stages; input video of the face, face anti-spoofing module, face and landmark detection, preprocessing, facial feature extraction and face output that include identification or verification result. A summary of implementation of deep learning within VFR steps is discussed. Finally, some directions for future research are also discussed. © 2022, Penerbit Universiti Kebangsaan Malaysia. All rights reserved.
publisher Penerbit Universiti Kebangsaan Malaysia
issn 18235670
language English
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