Real Time Cnn Based Detection of Face Mask Using Mobilenetv2 to Prevent Covid-19

Abstract

COVID-19 is also known as Corona virus, is spreading rapidly throughout the world. The World Health Organization (WHO) has released precautionary measures to reduce the virus spread among the people. Among those measures, mask   is the prior precaution of all. So, to solve this various models for face detection are created using different algorithms and techniques.  The approach which is put forward in the paper utilizes Deep learning, OpenCV, TenserFlow and Keras which helps in detection of faces with mask. With the help of this model we ensure the safety. The approach for face detector we have used CNN and MobileNetV2 architecture as the classifier, it is light weight, uses less parameters and it can be used in embedded systems (raspberry pi, Onion  Omega2 ) to carry out real time mask detection. The technique used in the paper gives an accuracy of   0.96 and F1-score of 0.92.In this paper, the dataset is collected through different sources which can be put to use for further modern models by various researchers such as facial landmarks, facial features and face recognition for detection process.