Covid Face Mask Detection Using Neural Networks

It has often been attributed that face masks can prevent the spread of COVID-19. Many scientists argue that it prevents virus-carrying droplets from reaching other hosts (people) while coughing and sneezing. This helps break the chain of spread. However, people do not like to cover their face with a proper face mask and some of them do not know how to wear it properly. Checking it manually for a large group of people, especially at a crowded place like a train station, theater, classroom or an airport, can be time-consuming and expensive. Also, people can be biased and gullible. Therefore, an automated, accurate and reliable system is required for the task. To train the system adequately, lots of data is required: images. The system should recognize if a person is not wearing a face mask at all, wearing it improperly or if the one is wearing it properly. In this paper, we are using MobileNetv2, which is a convolutional neural network (CNN) based architecture, to build such a face mask detection/recognition model. The developed model can classify people who are wearing masks, not properly wearing and not wearing it with an accuracy of 97.25 percent.