Abstract
With the onset of the COVID-19 pandemic, the entire world is in chaos and is talking about novel ways to prevent virus spread. People around the world are wearing masks as a precautionary measure to prevent catching this infection. While some are following and taking this measure, some are not still following despite official advice from the government and public health agencies. In this paper, a face mask detection model that can accurately detect whether a person is wearing a mask or not is proposed and implemented. The model architecture uses MobileNetV2, which is a lightweight convolutional neural network, therefore requires less computational power and can be easily embedded in computer vision systems and mobile. As a result, it can create a low-cost mask detector system that can help to identify whether a person is wearing a mask or not and act as a surveillance system as it works for both real-time images and videos. The face detector model achieved high accuracy of 99.98% on training data, 99.56% on validation data, and 99.75% on testing data.
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Taneja, S., Nayyar, A., Vividha, Nagrath, P. (2021). Face Mask Detection Using Deep Learning During COVID-19. In: Singh, P.K., Wierzchoń, S.T., Tanwar, S., Ganzha, M., Rodrigues, J.J.P.C. (eds) Proceedings of Second International Conference on Computing, Communications, and Cyber-Security. Lecture Notes in Networks and Systems, vol 203. Springer, Singapore. https://doi.org/10.1007/978-981-16-0733-2_3
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DOI: https://doi.org/10.1007/978-981-16-0733-2_3
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