Explainable Deep Learning for Face Mask Detection

The COVID-19 pandemic has radically changed our daily habits. One of these habits introduced is the use of a face mask, to avoid the spread of the infection, to be used especially in closed and crowded environments. To protect people and to counter the spread of COVID-19, in this paper we propose a method to automatically verify from images whether people are wearing a face mask. We designed a deep learning network to classify whether an image under analysis is with a mask or without a face mask. With the aim to provide explainability to the classifier decision, the proposed method is able to localise the areas of the image under analysis symptomatic of a certain prediction: in this way it is possible to understand the reason why the model predicts a certain label. The experimental analysis considers 7553 (with mask and without mask) images showing that the proposed approach is able to obtain interesting performances in face mask detection.