A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
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 indivi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/19/8781 |
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| author | Mohamed Mahmoud Mahmoud SalahEldin Kasem Hyun-Soo Kang |
| author_facet | Mohamed Mahmoud Mahmoud SalahEldin Kasem Hyun-Soo Kang |
| author_sort | Mohamed Mahmoud |
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| description | 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. |
| format | Article |
| id | doaj-art-74bdcb7d12cb4fe2a14af2c338defbc5 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-74bdcb7d12cb4fe2a14af2c338defbc52025-08-20T01:47:44ZengMDPI AGApplied Sciences2076-34172024-09-011419878110.3390/app14198781A Comprehensive Survey of Masked Faces: Recognition, Detection, and UnmaskingMohamed Mahmoud0Mahmoud SalahEldin Kasem1Hyun-Soo Kang2Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of KoreaDepartment of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of KoreaDepartment of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of KoreaMasked 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.https://www.mdpi.com/2076-3417/14/19/8781masked face recognitionmasked face identificationmasked face verificationface mask removalface unmaskingface mask recognition |
| spellingShingle | Mohamed Mahmoud Mahmoud SalahEldin Kasem Hyun-Soo Kang A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking Applied Sciences masked face recognition masked face identification masked face verification face mask removal face unmasking face mask recognition |
| title | A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking |
| title_full | A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking |
| title_fullStr | A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking |
| title_full_unstemmed | A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking |
| title_short | A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking |
| title_sort | comprehensive survey of masked faces recognition detection and unmasking |
| topic | masked face recognition masked face identification masked face verification face mask removal face unmasking face mask recognition |
| url | https://www.mdpi.com/2076-3417/14/19/8781 |
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