Deep Learning for Real-Time Face Mask Detection

ABSTRACT

The corona virus (COVID19) pandemic requires immediate action to avoid adverse effects on local health and the global economy. Due to the effects of COVID19, most of the people lives have been reversed. In the absence of effective antivirals and inadequate medical resources, UN agencies propose a number of measures to regulate infection rates and prevent the limited medical resources from being exhausted. Wearing a face mask is a type of the non-pharmaceutical intervention techniques that can block the primary care of viral droplets ejected by an infected person. According to government basics, it is important for everyone in every country to wear a mask. The government recommends wearing a mask, but many do not. Mask detection is very important in this situation. To contribute to community health, this study aims to develop highly accurate and timely techniques for detecting non-face masks in public and encouraging people to use them. It is said that "Increase the number of people who wear masks correctly and reduce the number of infected people". Starting with MobileNet V2 as a baseline, we used the concept of transfer learning to fuse high levels of linguistic data during mask recognition. For the face detection module, we used Caffe Model in conjunction with OpenCV's DNN module. The anticipated model's remarkable performance makes it ideal for live video police work equipment that detects face masks in real - time. © 2022 IEEE.