Abstract
Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. In order to safely live with the virus while effectively reducing its spread, the use of face masks has become ubiquitous. Indeed, several countries enforced compulsory face mask policies in public areas. Therefore, it is important to provide automatic solutions for masked/not-masked faces detection. In this work, we proposed a face mask detection method based on deep convolutional neural networks (CNNs) in an uncontrolled environment. In fact, the proposed method aims to locate not-masked faces in a video stream. Therefore, we performed a face detection based on the combination of multi-scale CNN feature maps. Then, we classified each face as masked-face or not-masked face. The main contribution of the proposed method is to reduce confusion between detected object classes by introducing a two steps face mask detection process. The experimental study was conducted on the multi-constrained public dataset “Face Mask Dataset” and the Simulated Masked Face Dataset (SMFD). The achieved results reveal the performance of our face mask detection method in an uncontrolled environment.
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Dammak, S., Mliki, H., Fendri, E. (2022). Deep Face Mask Detection: Prevention and Mitigation of COVID-19. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_2
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