  Identification of people with elevated body temperature can reduce or
dramatically slow down the spread of infectious diseases like COVID-19. We
present a novel fever-screening system, F3S, that uses edge machine learning
techniques to accurately measure core body temperatures of multiple individuals
in a free-flow setting. F3S performs real-time sensor fusion of visual camera
with thermal camera data streams to detect elevated body temperature, and it
has several unique features: (a) visual and thermal streams represent very
different modalities, and we dynamically associate semantically-equivalent
regions across visual and thermal frames by using a new, dynamic alignment
technique that analyzes content and context in real-time, (b) we track people
through occlusions, identify the eye (inner canthus), forehead, face and head
regions where possible, and provide an accurate temperature reading by using a
prioritized refinement algorithm, and (c) we robustly detect elevated body
temperature even in the presence of personal protective equipment like masks,
or sunglasses or hats, all of which can be affected by hot weather and lead to
spurious temperature readings. F3S has been deployed at over a dozen large
commercial establishments, providing contact-less, free-flow, real-time fever
screening for thousands of employees and customers in indoors and outdoor
settings.
