Face Mask Detection from Live-Stream Surveillance Video using Convolutional Neural Network

The coronavirus is a contagious disease and can spread very rapidly if the proper measures are not taken. Though the invention of the vaccines against coronavirus has given a sigh of relief, however, the complete eradication still looks a very long way to go. With the presence of the new variants of the coronavirus, the risk of the spread still remains. Among several guidelines given by the WHO and healthcare practitioners, facemasks have been one of the most effective ways to prevent the spread of the virus. However, some people usually ignore or forget to follow these guidelines especially in public places such as offices, shopping malls, etc. The number of people in such places is usually high and facemask is a factor to consider against the spread of the virus. Therefore, to hinder the spread of the virus, people with no facemask must be identified and notified. This research proposes a convolutional neural network-based deep learning model for detecting the people without facemasks using the frames captured from the live-stream surveillance video. The research primarily focuses on the facemask detection module of the proposed system. The data for this study contains almost 1500 images for masked and without mask faces. The proposed model has been implemented using two different optimizers. The RMSprop optimizer-based model outperforms the Adam optimizer-based model. The accuracy achieved by RMSprop based model was 92.27% and the accuracy achieved by Adam optimizer-based model was 85.1%.