  With the increased attention on thermal imagery for Covid-19 screening, the
public sector may believe there are new opportunities to exploit thermal as a
modality for computer vision and AI. Thermal physiology research has been
ongoing since the late nineties. This research lies at the intersections of
medicine, psychology, machine learning, optics, and affective computing. We
will review the known factors of thermal vs. RGB imaging for facial emotion
recognition. But we also propose that thermal imagery may provide a
semi-anonymous modality for computer vision, over RGB, which has been plagued
by misuse in facial recognition. However, the transition to adopting thermal
imagery as a source for any human-centered AI task is not easy and relies on
the availability of high fidelity data sources across multiple demographics and
thorough validation. This paper takes the reader on a short review of machine
learning in thermal FER and the limitations of collecting and developing
thermal FER data for AI training. Our motivation is to provide an introductory
overview into recent advances for thermal FER and stimulate conversation about
the limitations in current datasets.
