Face Mask Detection and Temperature Scanning for the Covid-19 Surveillance System

Abstract

Face mask recognition has grown significantly in recent years as a result of Corona's insistence on its numerous applications in law enforcement, security, and other commercial applications. A unique technique for performing face new line detection and face mask identification is presented. The proposed method employs a YoLo technique to recognise the objects like face masks in pictures and videos as a measure for COVID-19 precaution. Extensive testing on datasets and performance assessment of the suggested approaches are demonstrated. Furthermore, we used a symbolic method to successfully maintain inter and intra class differences in face mask detection. The proposed work is being created as a prototype to monitor temperature and identify masks for individuals. The first technique employs a temperature sensor to detect the body's current temperature. In the second way, the work is aimed at offering a safety mechanism for individuals in order to avoid COVID-19. Extensive experimentation on 50 different image datasets was carried out to assess the performance of the suggested technique. For ten random trials, we experimented with different training and testing percentages. Based on the data, we conclude that the symbolic method produces better outcomes than the conventional one.