Open Access

ARTICLE

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 Mehmood^2,*, 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:

Computers, Materials & Continua 2022, 71(2), 4151-4166. https://doi.org/10.32604/cmc.2022.017865

Received 15 February 2021; Accepted 07 September 2021; Issue published 07 December 2021

Abstract

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.

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