Sequential Convolutional Neural Network based Face Mask Detection

Abstract

Due to the COVID-19 pandemic, we have encountered signs such as "No entry without face mask" in public spaces such as theatres, restaurants, shopping malls, other places. The Face mask is one among the necessary preventive measures taken against COVID-19. Many doctors and scientists have suggested the continued use of face masks even after the vaccination drive. This new change in the way of life necessitates the use of a face mask in public places. Open public spaces cannot be monitored but enclosed public spaces can be monitored. The passageway of these public spaces should monitor if an individual has put on a face mask. Without manual supervision, the CNN algorithm could potentially be used to check the face mask of the persons entering the enclosed public spaces. In this paper, a Convolutional Neural Network prototype is constructed using TensorFlow, OpenCV and, Keras to detect, if an individual has put on a face mask to protect themselves. At the entrance of these public spaces, a monitor scans the faces of the people entering and detects if they are wearing a facemask using the CNN deep learning model. We collect data of images and pre-process them to develop a CNN prototype. The above deep learning algorithm could be coordinated with the doorways of public places to ensure, no person without a face mask enters the space.