Convolutional Neural Network-based Architecture for Detecting Face Mask in Crowded Areas

Abstract

After the invasion of the Covid-19 virus, governments started containing the spread of the virus by forcing people to wear face masks in public places. Therefore, automatic face mask detection has become very important to limit the virus spread. Unfortunately, existing methods present limited performance in accurately detecting masks in crowded areas due to the significant number of faces per scene. In order to tackle this challenge, we propose a two-stage neural network-based architecture that can accurately detect face masks in crowded environments. Several simulations have been conducted to investigate the efficiency of the proposed architecture and the results show a high accuracy of detection that can reach up to 96.5%.