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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References
Chen, H., Chen, Y., Tian, X., Jiang, R.: A cascade face spoofing detector based on face anti-spoofing R-CNN and improved Retinex LBP. IEEE Access 7, 170116–170133 (2019)
Daniell, C.: Detect faces and determine whether people are wearing mask (2020). https://github.com/AIZOOTech/FaceMaskDetection
Deng, J., Guo, J., Zhou, Y., Yu, J., Kotsia, I., Zafeiriou, S.: RetinaFace: single-stage dense face localisation in the wild. arXiv preprint arXiv:1905.00641 (2019)
Ge, S., Li, J., Ye, Q., Luo, Z.: Detecting masked faces in the wild with LLE-CNNs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2682–2690 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huang, C., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. lancet 395(10223), 497–506 (2020)
Jiang, M., Fan, X.: RetinaMask: a face mask detector. arXiv preprint arXiv:2005.03950 (2020)
Jin, Q., Mu, C., Tian, L., Ran, F.: A region generation based model for occluded face detection. Procedia Comput. Sci. 174, 454–462 (2020)
Joshi, A.S., Joshi, S.S., Kanahasabai, G., Kapil, R., Gupta, S.: Deep learning framework to detect face masks from video footage. In: 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 435–440. IEEE (2020)
Li, H., Alghowinem, S., Caldwell, S., Gedeon, T.: Interpretation of occluded face detection using convolutional neural network. In: 2019 IEEE 23rd International Conference on Intelligent Engineering Systems (INES), pp. 000165–000170. IEEE (2019)
Loey, M., Manogaran, G., Taha, M.H.N., Khalifa, N.E.M.: A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. Measurement 167, 108288 (2021)
Mliki, H., Dammak, S., Fendri, E.: An improved multi-scale face detection using convolutional neural network. Signal Image Video Process. 14(7), 1345–1353 (2020). https://doi.org/10.1007/s11760-020-01680-w
Prajnasb: observations (2020). https://github.com/prajnasb/observations
Putro, M.D., Nguyen, D.-L., Jo, K.-H.: Real-time multi-view face mask detector on edge device for supporting service robots in the COVID-19 pandemic. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawiński, B. (eds.) ACIIDS 2021. LNCS (LNAI), vol. 12672, pp. 507–517. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73280-6_40
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)
Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Snyder, S.E., Husari, G.: Thor: a deep learning approach for face mask detection to prevent the COVID-19 pandemic. In: SoutheastCon 2021, pp. 1–8. IEEE (2021)
Su, Y., Wan, X., Guo, Z.: Robust face detector with fully convolutional networks. In: Lai, J.-H., et al. (eds.) PRCV 2018. LNCS, vol. 11258, pp. 207–218. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03338-5_18
WHO: World Health Organization: Who characterizes COVID-19 as a pandemic (2020). https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-mediabriefing-on-covid-19-11-march-2020
Wu, W., Yin, Y., Wang, X., Xu, D.: Face detection with different scales based on faster R-CNN. IEEE Trans. Cybern. 49(11), 4017–4028 (2018)
Yang, S., Luo, P., Loy, C.C., Tang, X.: WIDER FACE: a face detection benchmark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5525–5533 (2016)
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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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