Towards smart surveillance as an aftereffect of COVID-19 outbreak for recognition of face masked individuals using YOLOv3 algorithm

The eruption of COVID-19 pandemic has led to the blossoming usage of face masks among individuals in the communal settings. To prevent the transmission of the virus, a mandatory mask-wearing rule in public areas has been enforced. Owing to the use of face masks in communities at different workplaces, an effective surveillance seems essential because several security analyses indicate that face masks may be used as a tool to hide the identity. Therefore, this work proposes a framework for the development of a smart surveillance system as an aftereffect of COVID-19 for recognition of individuals behind the face mask. For this purpose, transfer learning approach has been employed to train the custom dataset by YOLOv3 algorithm in the Darknet neural network framework. Moreover, to demonstrate the competence of YOLOv3 algorithm, a comparative analysis with YOLOv3-tiny has been presented. The simulated results verify the robustness of YOLOv3 algorithm in the recognition of individuals behind the face mask. Also, YOLOv3 algorithm achieves a mAP of 98.73% on custom dataset, outperforming YOLOv3-tiny by approximately 62%. Moreover, YOLOv3 algorithm provides adequate speed and accuracy on small faces.