Classification and Detection of Automated Facial Mask to COVID-19 based on Deep CNN Model

The rapid spread of the COVID-19 coronavirus over the past two years has been a major threat to global health. One of the most important contributors to the rapid spread of the virus is human-to-human contact. Wearing a mask in a public place is the most notable one. Finding a way to identify people wearing masks in public places is an important step toward reducing the likelihood of the virus spreading. To counteract these problems and put a halt to the spread of this infectious disease, a system for automatic face mask detection based on deep learning (DL) approach has been developed. Deep convolution neural network (DCNN) models are used in this study to effectively detect face masks. We tested these models using datasets, with face mask detection datasets serving as the standard.A DCNN is used to construct the training and testing masked datasets. Our trained models had an accuracy of almost 96 percent. So, the proposed approach may be readily used to both masked and without masked facial recognition & detection systems developed for security authatication.