Convolutional neural network architecture for detecting facemask and social distancing: a preventive measure for COVID19

• Main Authors: Malik, Najeeb Ur Rehman, Abu Bakar, Syed A. R., Sheikh, Usman Ullah, Airij, Awais Gul
• Format: Book Section
• Published: Springer Science and Business Media Deutschland GmbH 2022
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/100452/; http://dx.doi.org/10.1007/978-981-16-8129-5_144

COVID-19 is a life-threatening virus which affected people at a global level in just a matter of few months and is highly contagious. In order to reduce its spread, SOPs must be followed, such as washing hands, wearing face masks, and maintaining social distance. Hence, to aid the strict follow up of SOPs, this paper proposes a system to detect whether the people are wearing face masks and maintaining social distance or not in order to break the chain of COVID 19. The proposed system uses Deep Learning (DL) model based on Convolutional Neural Network (CNN) architecture for training the facemask detector and OpenPose 2D skeleton extraction technique for detecting social distance. A DL model based on a 7-layered CNN architecture was proposed in this research to detect masked and unmasked faces. Based on the proposed technique, 99.98% validation and 99.98% testing accuracies were achieved. In addition to that, the maintenance of social distance which is the new normal nowadays was also detected using the images obtained from the internet as currently, there is no such database available for detecting social distancing.