Deep learning techniques for detecting and recognizing face masks: A survey - Alturki, Rahaf; Alharbi, Maali; AlAnzi, Ftoon; Albahli, Saleh (Frontiers in Public Health, 2022)
The year 2020 brought a lot of changes to the lives of people all over the world with the out break of COVID-19, we saw lockdowns for months and a lot of death which set the world economy back miles. As research was done to create vaccines and cures that will eradicate the virus, precautionary measures were imposed on people to help reduce the spread the disease, these measures include; washing of hands, appropriate distancing in social gatherings and wearing of masks to cover the face and nose. But due to human error, most people fail to adhere to this face mask rule and it has to be controlled using artificial intelligence. In this work, we carried out a survey on MFR and OFR deep learning techniques used to detect face mask being worn. The major problem faced by these models is a lot of times people wear face masks incorrectly by either not covering the nose or mouth which is the same as not wearing, the deep learning algorithms detected the covered features on the face to ensure that the correct parts of the face are covered and had amazing and effective results.