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A Real-Time Integrated Face Mask Detector to Curtail Spread of Coronavirus

Shilpa Sethi1, Mamta Kathuria1,*, Trilok Kaushik2

1 Department of Computer Applications, J. C. Bose University of Science and Technology, Faridabad, 121102, India
2 R&D Department, Samsung India Pvt., Ltd., Noida, 210301, India

* Corresponding Author: Mamta Kathuria. Email: email

(This article belongs to the Special Issue: Computer Modelling of Transmission, Spread, Control and Diagnosis of COVID-19)

Computer Modeling in Engineering & Sciences 2021, 127(2), 389-409. https://doi.org/10.32604/cmes.2021.014478

Abstract

Effective strategies to control COVID-19 pandemic need high attention to mitigate negatively impacted communal health and global economy, with the brim-full horizon yet to unfold. In the absence of effective antiviral and limited medical resources, many measures are recommended by WHO to control the infection rate and avoid exhausting the limited medical resources. Wearing mask is among the non-pharmaceutical intervention measures that can be used as barrier to primary route of SARS-CoV2 droplets expelled by presymptomatic or asymptomatic individuals. Regardless of discourse on medical resources and diversities in masks, all countries are mandating coverings over nose and mouth in public areas. Towards contribution of public health, the aim of the paper is to devise a real-time technique that can efficiently detect non mask faces in public and thus enforce to wear mask. The proposed technique is ensemble of one stage and two stage detectors to achieve low inference time and high accuracy. We took ResNet50 as a baseline model and applied the concept of transfer learning to fuse high level semantic information in multiple feature maps. In addition, we also propose a bounding box transformation to improve localization performance during mask detection. The experiments are conducted with three popular baseline models namely ResNet50, AlexNet and MobileNet. We explored the possibility of these models to plug-in with the proposed model, so that highly accurate results can be achieved in less inference time. It is observed that the proposed technique can achieve high accuracy (98.2%) when implemented with ResNet50. Besides, the proposed model can generate 11.07% and 6.44% higher precision and recall respectively in mask detection when compared to RetinaFaceMask detector.

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APA Style
Sethi, S., Kathuria, M., Kaushik, T. (2021). A real-time integrated face mask detector to curtail spread of coronavirus. Computer Modeling in Engineering & Sciences, 127(2), 389-409. https://doi.org/10.32604/cmes.2021.014478
Vancouver Style
Sethi S, Kathuria M, Kaushik T. A real-time integrated face mask detector to curtail spread of coronavirus. Comput Model Eng Sci. 2021;127(2):389-409 https://doi.org/10.32604/cmes.2021.014478
IEEE Style
S. Sethi, M. Kathuria, and T. Kaushik "A Real-Time Integrated Face Mask Detector to Curtail Spread of Coronavirus," Comput. Model. Eng. Sci., vol. 127, no. 2, pp. 389-409. 2021. https://doi.org/10.32604/cmes.2021.014478

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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