Mamdouh M. Gomaa¹, Alaa Elnashar¹, Mahmoud M. Eelsherif², and Alaa M. Zaki¹, ¹Minia University, Egypt, ²Sinai university, Egypt

In current times, after the rapid expansion and spread of the COVID-19 outbreak globally, people have experienced severe disruption to their daily lives. One idea to manage the out-break is to enforce people wear a face mask in public places. Therefore, automated and efficient face detection methods are essential for such enforcement. In this paper, a face mask detection model for images has been presented which classifies the images as “with mask” and “without mask”. The model is trained and evaluated using the three datasets Real-World Masked Face Dataset (RMFD), Simulated Masked Face Dataset (SMFD), and Labeled Faces in the Wild (LFW), and attained a performance accuracy rate of 99.72% for first dataset, and 100% for the second and third datasets. This work can be utilized as a digitized scanning tool in schools, hospitals, banks, and airports, and many other public or commercial locations.

COVID-19, image processing, Deep learning, Convolutional neural network (CNN).