A Unified Analysis of Masked Face Recognition & Social Distancing Detection

Facial recognition is an important application in today’s world but given the importance of wearing face masks due to the COVID-19 pandemic, it is essential that masked face recognition performs well. It is also equally important to ensure that protective measures such as wearing face masks and social distancing are being followed. Hence, this paper introduces an approach unifying masked face recognition, mask detection, and social distancing detection into a single system. The proposed method uses RetinaFace for face detection, VGG-19 for image classification, as well as VGGFace and SVC for facial recognition. As part of experimental analysis, CNN models such as ResNet50 and Inceptionv3 were used to evaluate mask detection while FaceNet and ArcFace were used to evaluate masked face recognition. The entire system was evaluated using accuracy, precision, recall, and F1-score. The obtained results indicate that the proposed system performs efficiently.