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A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
by
Mohamed Mahmoud
Mohamed Mahmoud 1,2,
Mahmoud SalahEldin Kasem
Mahmoud SalahEldin Kasem 1,3 and
Hyun-Soo Kang
Hyun-Soo Kang
Prof. Dr. Hyun-Soo Kang received his B.S. degree in Electronic Engineering from Kyoungpook National [...]
Prof. Dr. Hyun-Soo Kang received his B.S. degree in Electronic Engineering from Kyoungpook National University, Republic of Korea, in 1991, and his M.S. and Ph.D. degrees in Electrical and Electronics Engineering from the Korea Advanced Institute of Science and Technology (KAIST) in 1994 and 1999, respectively. From 1999 to 2005, he was with Hynix Semiconductor Co., Ltd.; the Electronics and Telecommunications Research Institute (ETRI); and Chungang University. He joined the College of Electrical and Computer Engineering of Chungbuk National University, Chungbuk, Republic of Korea, in March 2005. His research interests include computer vision and image processing.
1,*
1
Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
2
Information Technology Department, Faculty of Computers and Information, Assiut University, Assiut 71526, Egypt
3
Multimedia Department, Faculty of Computers and Information, Assiut University, Assiut 71526, Egypt
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(19), 8781; https://doi.org/10.3390/app14198781 (registering DOI)
Submission received: 31 August 2024
/
Revised: 24 September 2024
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Accepted: 26 September 2024
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Published: 28 September 2024
Abstract
Masked face recognition (MFR) has emerged as a critical domain in biometric identification, especially with the global COVID-19 pandemic, which introduced widespread face masks. This survey paper presents a comprehensive analysis of the challenges and advancements in recognizing and detecting individuals with masked faces, which has seen innovative shifts due to the necessity of adapting to new societal norms. Advanced through deep learning techniques, MFR, along with face mask recognition (FMR) and face unmasking (FU), represents significant areas of focus. These methods address unique challenges posed by obscured facial features, from fully to partially covered faces. Our comprehensive review explores the various deep learning-based methodologies developed for MFR, FMR, and FU, highlighting their distinctive challenges and the solutions proposed to overcome them. Additionally, we explore benchmark datasets and evaluation metrics specifically tailored for assessing performance in MFR research. The survey also discusses the substantial obstacles still facing researchers in this field and proposes future directions for the ongoing development of more robust and effective masked face recognition systems. This paper serves as an invaluable resource for researchers and practitioners, offering insights into the evolving landscape of face recognition technologies in the face of global health crises and beyond.
Share and Cite
MDPI and ACS Style
Mahmoud, M.; Kasem, M.S.; Kang, H.-S.
A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking. Appl. Sci. 2024, 14, 8781.
https://doi.org/10.3390/app14198781
AMA Style
Mahmoud M, Kasem MS, Kang H-S.
A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking. Applied Sciences. 2024; 14(19):8781.
https://doi.org/10.3390/app14198781
Chicago/Turabian Style
Mahmoud, Mohamed, Mahmoud SalahEldin Kasem, and Hyun-Soo Kang.
2024. "A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking" Applied Sciences 14, no. 19: 8781.
https://doi.org/10.3390/app14198781
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