  The COVID-19 pandemic affects every area of daily life globally. To avoid the
spread of coronavirus and retrieve the daily normal worldwide, health
organizations advise social distancing, wearing face mask, and avoiding
touching face. Based on these recommended protective measures, we developed a
deep learning-based computer vision system to help prevent the transmission of
COVID-19. Specifically, the developed system performs face mask detection,
face-hand interaction detection, and measures social distance. For these
purposes, we collected and annotated images that represent face mask usage and
face-hand interaction in the real world. We presented two different face
datasets, namely Unconstrained Face Mask Dataset (UFMD) and Unconstrained Face
Hand Dataset (UFHD). We trained the proposed models on our own datasets and
evaluated them on both our datasets and already existing datasets in the
literature without performing any adaptation on these target datasets. Besides,
we proposed a distance measurement module to track social distance between
people. Experimental results indicate that UFMD and UFHD represent the
real-world's diversity well. The proposed system achieved very high performance
and generalization capacity in a real-world scenario for unseen data from
outside the training data to detect face mask usage and face-hand interaction,
and satisfactory performance in the case of tracking social distance. Presented
UFMD and UFHD datasets will be available at
https://github.com/iremeyiokur/COVID-19-Preventions-Control-System.
