CNN Based COVID-19 Prevention System

Abstract

In order to effectively prevent the spread of COVID19 virus, almost everyone wears a mask during coronavirus epidemic. This almost makes conventional facial recognition technology ineffective in many cases, such as community access control, face access control, facial attendance, facial security checks at train stations, etc. Therefore, it is very urgent to improve the recognition performance of the existing face recognition technology on the masked faces. Most current advanced face recognition approaches are designed based on deep learning, which depend on a large number of face samples. However, at present, there are no publicly available masked face recognition datasets. Compared to other datasets, Real-world Masked Face Recognition Dataset (RMFRD) is currently the world’s largest real-world masked face dataset. Various COVID19 prevention measures are undertaken such as wearing mask, sanitization, social distancing and temperature monitoring. An artificial intelligent IOT (Internet of Things) system with temperature monitoring, auto sanitization, mask detection is proposed. In this system, the machine is connected to a server by which the admin can monitor everything live from any place. The system also has face recognition feature by which the registered visitors, students can recognize separately and admin can maintain proper student database with temperature, auto sanitization system for door opening and closing system.