Paper
19 October 2023 A swift and efficient method for face mask recognition based on off-the-shelf models
Chongxiao Qu, Jiping Zheng, Yongjin Zhang, Changjun Fan, Shuo Liu
Author Affiliations +
Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270917 (2023) https://doi.org/10.1117/12.2685006
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China
Abstract
Since the outbreak of COVID-19, the widespread use of face masks has prompted significant research efforts in face mask recognition. Based on the previous work, it is crucial to develop a face mask recognition system swiftly using offthe-shelf techniques, while it is equally important to ensure that the system's performance and efficiency meet the requirements of real-life scenarios. In this paper, we propose a two-fold face mask recognition method, by first detecting faces in an image using YuNet and then recognizing whether the faces are wearing a mask or not using MobileNet. After validation on a self-built dataset, compared to similar related works, our method can effectively improve the face detection rate, even in situations of occlusion and blurring, and the detection and recognition efficiency has been significantly improved too.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chongxiao Qu, Jiping Zheng, Yongjin Zhang, Changjun Fan, and Shuo Liu "A swift and efficient method for face mask recognition based on off-the-shelf models", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270917 (19 October 2023); https://doi.org/10.1117/12.2685006
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KEYWORDS
Facial recognition systems

Object detection

Education and training

Machine learning

Detection and tracking algorithms

Convolution

Data modeling

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