  In the last two years, millions of lives have been lost due to COVID-19.
Despite the vaccination programmes for a year, hospitalization rates and deaths
are still high due to the new variants of COVID-19. Stringent guidelines and
COVID-19 screening measures such as temperature check and mask check at all
public places are helping reduce the spread of COVID-19. Visual inspections to
ensure these screening measures can be taxing and erroneous. Automated
inspection ensures an effective and accurate screening. Traditional approaches
involve identification of faces and masks from visual camera images followed by
extraction of temperature values from thermal imaging cameras. Use of visual
imaging as a primary modality limits these applications only for good-lighting
conditions. The use of thermal imaging alone for these screening measures makes
the system invariant to illumination. However, lack of open source datasets is
an issue to develop such systems. In this paper, we discuss our work on using
machine learning over thermal video streams for face and mask detection and
subsequent temperature screening in a passive non-invasive way that enables an
effective automated COVID-19 screening method in public places. We open source
our NTIC dataset that was used for training our models and was collected at 8
different locations. Our results show that the use of thermal imaging is as
effective as visual imaging in the presence of high illumination. This
performance stays the same for thermal images even under low-lighting
conditions, whereas the performance with visual trained classifiers show more
than 50% degradation.
