YOLOv3-based mask and face recognition algorithm for individual protection applications

To combat the spread of the COVID-19 pandemic, it is essential to strictly obey social distancing measures, as well as have the possibility to possess and wear personal protective equipment. This paper proposes a mask and face recognition algorithm based on YOLOv3 for individual protection applications. The proposed method processes images directly in raw data format input to a neural network trained with deep learning techniques. System training was performed on a set of images appropriately obtained from the MAFA dataset by selecting those with surgical masks for a total of about 6,000 cases. The performances obtained indicate 84% accuracy in recognizing a mask and 96% in the case of a face.