  Recent research is trying to leverage occupants' demand in the building's
control loop to consider individuals' well-being and the buildings' energy
savings. To that end, a real-time feedback system is needed to provide data
about occupants' comfort conditions that can be used to control the building's
heating, cooling, and air conditioning (HVAC) system. The emergence of thermal
imaging techniques provides an excellent opportunity for contactless data
gathering with no interruption in occupant conditions and activities. There is
increasing attention to infrared thermal camera usage in public buildings
because of their non-invasive quality in reading the human skin temperature.
However, the state-of-the-art methods need additional modifications to become
more reliable. To capitalize potentials and address some existing limitations,
new solutions are required to bring a more holistic view toward non-intrusive
thermal scanning by leveraging the benefit of machine learning and image
processing. This research implements an automated approach to collect and
register simultaneous thermal and visual images and read the facial temperature
in different regions. This paper also presents two additional investigations.
First, through utilizing IButton wearable thermal sensors on the forehead area,
we investigate the reliability of an in-expensive thermal camera (FLIR Lepton)
in reading the skin temperature. Second, by studying the false-color version of
thermal images, we look into the possibility of non-radiometric thermal images
for predicting personalized thermal comfort. The results shows the strong
performance of Random Forest and K-Nearest Neighbor prediction algorithms in
predicting personalized thermal comfort. In addition, we have found that
non-radiometric images can also indicate thermal comfort when the algorithm is
trained with larger amounts of data.
