A Review on Face Mask Recognition
This review offers a comprehensive and in-depth analysis of face mask detection and recognition technologies, emphasizing their critical role in both public health and technological advancements. Existing detection methods are systematically categorized into three primary classes: feaRture-extractio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/387 |
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| author | Jiaonan Zhang Dong An Yiwen Zhang Xiaoyan Wang Xinyue Wang Qiang Wang Zhongqi Pan Yang Yue |
| author_facet | Jiaonan Zhang Dong An Yiwen Zhang Xiaoyan Wang Xinyue Wang Qiang Wang Zhongqi Pan Yang Yue |
| author_sort | Jiaonan Zhang |
| collection | DOAJ |
| description | This review offers a comprehensive and in-depth analysis of face mask detection and recognition technologies, emphasizing their critical role in both public health and technological advancements. Existing detection methods are systematically categorized into three primary classes: feaRture-extraction-and-classification-based approaches, object-detection-models-based methods and multi-sensor-fusion-based methods. Through a detailed comparison, their respective workflows, strengths, limitations, and applicability across different contexts are examined. The review underscores the paramount importance of accurate face mask detection, especially in response to global public health challenges such as pandemics. A central focus is placed on the role of datasets in driving algorithmic performance, addressing key factors, including dataset diversity, scale, annotation granularity, and modality. The integration of depth and infrared data is explored as a promising avenue for improving robustness in real-world conditions, highlighting the advantages of multimodal datasets in enhancing detection capabilities. Furthermore, the review discusses the synergistic use of real-world and synthetic datasets in overcoming challenges such as dataset bias, scalability, and resource scarcity. Emerging solutions, such as lightweight model optimization, domain adaptation, and privacy-preserving techniques, are also examined as means to improve both algorithmic efficiency and dataset quality. By synthesizing the current state of the field, identifying prevailing challenges, and outlining potential future research directions, this paper aims to contribute to the development of more effective, scalable, and robust face mask detection systems for diverse real-world applications. |
| format | Article |
| id | doaj-art-40b15b27c30d43f78d8b17fcb12ef425 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-40b15b27c30d43f78d8b17fcb12ef4252025-01-24T13:48:44ZengMDPI AGSensors1424-82202025-01-0125238710.3390/s25020387A Review on Face Mask RecognitionJiaonan Zhang0Dong An1Yiwen Zhang2Xiaoyan Wang3Xinyue Wang4Qiang Wang5Zhongqi Pan6Yang Yue7School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaInstitute of Modern Optics, Nankai University, Tianjin 300350, ChinaDrilling & Production Technology Research Institute, Chuanqing Drilling Engineering Company Limited, Guanghan 618300, ChinaInstitute of Modern Optics, Nankai University, Tianjin 300350, ChinaSchool of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaAngle AI (Tianjin) Technology Company Ltd., Tianjin 300450, ChinaDepartment of Electrical & Computer Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USASchool of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaThis review offers a comprehensive and in-depth analysis of face mask detection and recognition technologies, emphasizing their critical role in both public health and technological advancements. Existing detection methods are systematically categorized into three primary classes: feaRture-extraction-and-classification-based approaches, object-detection-models-based methods and multi-sensor-fusion-based methods. Through a detailed comparison, their respective workflows, strengths, limitations, and applicability across different contexts are examined. The review underscores the paramount importance of accurate face mask detection, especially in response to global public health challenges such as pandemics. A central focus is placed on the role of datasets in driving algorithmic performance, addressing key factors, including dataset diversity, scale, annotation granularity, and modality. The integration of depth and infrared data is explored as a promising avenue for improving robustness in real-world conditions, highlighting the advantages of multimodal datasets in enhancing detection capabilities. Furthermore, the review discusses the synergistic use of real-world and synthetic datasets in overcoming challenges such as dataset bias, scalability, and resource scarcity. Emerging solutions, such as lightweight model optimization, domain adaptation, and privacy-preserving techniques, are also examined as means to improve both algorithmic efficiency and dataset quality. By synthesizing the current state of the field, identifying prevailing challenges, and outlining potential future research directions, this paper aims to contribute to the development of more effective, scalable, and robust face mask detection systems for diverse real-world applications.https://www.mdpi.com/1424-8220/25/2/387face mask detectionobject detectionCOVID-19 |
| spellingShingle | Jiaonan Zhang Dong An Yiwen Zhang Xiaoyan Wang Xinyue Wang Qiang Wang Zhongqi Pan Yang Yue A Review on Face Mask Recognition Sensors face mask detection object detection COVID-19 |
| title | A Review on Face Mask Recognition |
| title_full | A Review on Face Mask Recognition |
| title_fullStr | A Review on Face Mask Recognition |
| title_full_unstemmed | A Review on Face Mask Recognition |
| title_short | A Review on Face Mask Recognition |
| title_sort | review on face mask recognition |
| topic | face mask detection object detection COVID-19 |
| url | https://www.mdpi.com/1424-8220/25/2/387 |
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