Masknet: Detecting Facial Coverings with Convolutional Neural Networks

The growing use of face masks for preventing respiratory diseases has created a demand for automated detection systems. In our study, we propose utilizing Convolutional Neural Networks (CNNs) to fulfill this need. To substantiate our proposal, we constructed a dataset comprising 3725 images of individuals wearing masks and 3828 images of individuals without masks. We labeled each picture with a value “1” for masks on faces in the images and “0” for those without them. The small parts of the photographs where people’s faces were obscured by the masks also needed marking so as not to mislead people about what is depicted by an image. For us to have the same type of data all over again, we had to change an image’s size.