Masked face recognition using deep learning: a review

Alzu’bi, A., Albalas, F., Al-Hadhrami, T. ORCID: 0000-0001-7441-604X, Bani Younis, L.B. and Bashayreh, A., 2021. Masked face recognition using deep learning: a review. Electronics, 10 (21): 2666. ISSN 2079-9292

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A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR.

Item Type: Journal article
Publication Title: Electronics
Creators: Alzu’bi, A., Albalas, F., Al-Hadhrami, T., Bani Younis, L.B. and Bashayreh, A.
Publisher: MDPI AG
Date: 31 October 2021
Volume: 10
Number: 21
ISSN: 2079-9292
Rights: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Divisions: Schools > School of Science and Technology
Record created by: Laura Ward
Date Added: 05 Nov 2021 11:08
Last Modified: 05 Nov 2021 11:08

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