Identification of with Face Mask and without Face Mask using Face Recognition Model

In this paper, we present a method for automatically detecting mask elements from faces and synthesizing the affected region with fine details while preserving the original structure of the face. Wearing face masks appears to be a promising approach for reducing COVID-19 spread. Effective recognition technologies are crucial in this situation to keep people’s faces hidden in limited places. As a result, in order to train deep learning models to distinguish between those wearing masks and those who aren’t, a huge dataset of masked faces is required. A technique has been devised for researching COVID-19 related behaviours and contamination processes. A potential method for minimising COVID-19 transmission through health education has been identified. With 96.70% accuracy, the suggested approach recognised faces with and without masks. They will give you a decent detection result with or without a mask.