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An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

Maha Farouk S. Sabir1, Irfan Mehmood2,*, Wafaa Adnan Alsaggaf3, Enas Fawai Khairullah3, Samar Alhuraiji4, Ahmed S. Alghamdi5, Ahmed A. Abd El-Latif6

1 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Saudi Arabia
2 Centre for Visual Computing, Faculty of Engineering and Informatics, University of Bradford, Bradford, U.K
3 Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, P.O. Box 23713, Saudi Arabia
4 Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
5 Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
6 Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, 32511, Egypt

* Corresponding Author: Irfan Mehmood. Email: email

(This article belongs to this Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)

Computers, Materials & Continua 2022, 71(2), 4151-4166.


Today, due to the pandemic of COVID-19 the entire world is facing a serious health crisis. According to the World Health Organization (WHO), people in public places should wear a face mask to control the rapid transmission of COVID-19. The governmental bodies of different countries imposed that wearing a face mask is compulsory in public places. Therefore, it is very difficult to manually monitor people in overcrowded areas. This research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places, by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of COVID-19. This paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or unmasked. The proposed framework is built by fine-tuning the state-of-the-art deep learning model, Faster-RCNN, and has been validated on a publicly available dataset named Face Mask Dataset (FMD) and achieving the highest average precision (AP) of 81% and highest average Recall (AR) of 84%. This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked faces. Moreover, this work applies to real-time and can be implemented in any public service area.


Cite This Article

M. Farouk S. Sabir, I. Mehmood, W. Adnan Alsaggaf, E. Fawai Khairullah, S. Alhuraiji et al., "An automated real-time face mask detection system using transfer learning with faster-rcnn in the era of the covid-19 pandemic," Computers, Materials & Continua, vol. 71, no.2, pp. 4151–4166, 2022.

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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