Review of Masked Face Recognition Based on Deep Learning
With the widespread adoption of face masks due to global health crises and heightened security concerns, traditional face recognition systems have struggled to maintain accuracy, prompting significant research into masked face recognition (MFR). Although various models have been proposed, a comprehe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/13/7/310 |
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| author | Bilal Saoud Abdul Hakim H. M. Mohamed Ibraheem Shayea Ayman A. El-Saleh Abdulaziz Alashbi |
| author_facet | Bilal Saoud Abdul Hakim H. M. Mohamed Ibraheem Shayea Ayman A. El-Saleh Abdulaziz Alashbi |
| author_sort | Bilal Saoud |
| collection | DOAJ |
| description | With the widespread adoption of face masks due to global health crises and heightened security concerns, traditional face recognition systems have struggled to maintain accuracy, prompting significant research into masked face recognition (MFR). Although various models have been proposed, a comprehensive and systematic understanding of recent deep learning (DL)-based approaches remains limited. This paper addresses this research gap by providing an extensive review and comparative analysis of state-of-the-art MFR techniques. We focus on DL-based methods due to their superior performance in real-world scenarios, discussing key architectures, feature extraction strategies, datasets, and evaluation metrics. This paper also introduces a structured methodology for selecting and reviewing relevant works, ensuring transparency and reproducibility. As a contribution, we present a detailed taxonomy of MFR approaches, highlight current challenges, and suggest potential future research directions. This survey serves as a valuable resource for researchers and practitioners seeking to advance the field of robust facial recognition in masked conditions. |
| format | Article |
| id | doaj-art-e466210fb402401fa0384a651c2ab784 |
| institution | DOAJ |
| issn | 2227-7080 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-e466210fb402401fa0384a651c2ab7842025-08-20T02:47:21ZengMDPI AGTechnologies2227-70802025-07-0113731010.3390/technologies13070310Review of Masked Face Recognition Based on Deep LearningBilal Saoud0Abdul Hakim H. M. Mohamed1Ibraheem Shayea2Ayman A. El-Saleh3Abdulaziz Alashbi4Department of Electrical Engineering, Faculty of Applied Sciences, University of Bouira, Bouira 10000, AlgeriaDepartment of Information Systems and Business Analytics, A’Sharqiyah University (ASU), Ibra 400, OmanDepartment of Electronics & Communications Engineering, Faculty of Electrical and Electronics Engineering, Istanbul Technical University (ITU), Istanbul 34469, TurkeyDepartment of Electrical Engineering and Computer Science, College of Engineering, A’Sharqiyah University (ASU), Ibra 400, OmanDepartment of Information Systems and Business Analytics, A’Sharqiyah University (ASU), Ibra 400, OmanWith the widespread adoption of face masks due to global health crises and heightened security concerns, traditional face recognition systems have struggled to maintain accuracy, prompting significant research into masked face recognition (MFR). Although various models have been proposed, a comprehensive and systematic understanding of recent deep learning (DL)-based approaches remains limited. This paper addresses this research gap by providing an extensive review and comparative analysis of state-of-the-art MFR techniques. We focus on DL-based methods due to their superior performance in real-world scenarios, discussing key architectures, feature extraction strategies, datasets, and evaluation metrics. This paper also introduces a structured methodology for selecting and reviewing relevant works, ensuring transparency and reproducibility. As a contribution, we present a detailed taxonomy of MFR approaches, highlight current challenges, and suggest potential future research directions. This survey serves as a valuable resource for researchers and practitioners seeking to advance the field of robust facial recognition in masked conditions.https://www.mdpi.com/2227-7080/13/7/310deep learningCNNface detectionobject detectiondatasetevaluation |
| spellingShingle | Bilal Saoud Abdul Hakim H. M. Mohamed Ibraheem Shayea Ayman A. El-Saleh Abdulaziz Alashbi Review of Masked Face Recognition Based on Deep Learning Technologies deep learning CNN face detection object detection dataset evaluation |
| title | Review of Masked Face Recognition Based on Deep Learning |
| title_full | Review of Masked Face Recognition Based on Deep Learning |
| title_fullStr | Review of Masked Face Recognition Based on Deep Learning |
| title_full_unstemmed | Review of Masked Face Recognition Based on Deep Learning |
| title_short | Review of Masked Face Recognition Based on Deep Learning |
| title_sort | review of masked face recognition based on deep learning |
| topic | deep learning CNN face detection object detection dataset evaluation |
| url | https://www.mdpi.com/2227-7080/13/7/310 |
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