Abstract
The COVID-19 can be transmitted by air droplets, aerosols, and other carriers, the spread of the virus can be effectively prevented by wearing masks in public. Therefore, it is meaningful to identify whether a mask is worn in particular places. In this paper, a method based on multi-task convolutional neural networks (MTCNN) and MobileNet algorithms is proposed to implement mask recognition on human face. Firstly, MTCNN is used to detect facial contours. Then the output image is used to train MobileNet model. By comparing the extracted facial feature data, the human with mask or not can be marked. The method has been tested in a 1.8 GHz Intel Core machine with 160 × 160 static images. Average accuracy rate of 94.73% and detection speed of 1.9 s are achieved.
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This research work was financially supported by the Youth Science Foundation of Liaoning Province, China.
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Cao, J., Pang, R., Ma, R., Qi, Y. (2021). Face Mask Recognition Based on MTCNN and MobileNet. In: Li, Y., Zhu, Q., Qiao, F., Fan, Z., Chen, Y. (eds) Advances in Simulation and Process Modelling. ISSPM 2020. Advances in Intelligent Systems and Computing, vol 1305. Springer, Singapore. https://doi.org/10.1007/978-981-33-4575-1_41
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DOI: https://doi.org/10.1007/978-981-33-4575-1_41
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