Comparative Analysis of various Machine and Deep Learning Models for Face Mask Detection using Digital Images

COVID-19 has majorly influenced life of all human beings worldwide. It is also affecting human's daily life style. During this pandemic situation, face masks have been used worldwide for protection against the disease of Coronavirus. Face masks are the most accepted and operative way to prevent the spread of Covid-19 cases. It is really very difficult to correctly monitor whether the individual have worn face mask or not in order to notify the victim in crowded and hustled places. The face mask detection process generally uses a camera to capture live streaming videos of crowd places and converting them into images. These images acts as input for facial mask recognition or detection systems. The systems for mask detection are developed with a toll-way gate which allows only if the person crossing it has a face mask worn on his/her face or it does not allow the person in. This paper intends to compare various machine and deep learning based models for binary classification of digital images into two classes of images with and without mask. For this, various parameters including Accuracy, Precision, Fl-Score and Recall are obtained for various models. In the experimental results and analysis section, it has been shown that CNN model outperforms the other state-of-the-art models in terms of above parameters.