We aim to identify humans in multimodal imagery by predicting the human long-wave infrared (LWIR) signature in a
variety of scenarios. By adapting Tanabe's thermocomfort model, we simulate human body heat flow both between
tissue layers (core, muscle, fat and skin) and between body segments (head, chest, upper arm, etc.). To assess the validity
of our implementation, we simulated the conditions described in actual human subject studies, and compared our results
to values reported in the literature. Inputs to the model include age, height, weight, clothing, physical activity and
ambient conditions, including temperature, humidity and wind velocity. Iteration of heat transport equations and a
thermoregulatory component yields temporal data of segment surface temperature. Our model was found to be in close
agreement with experimentally collected data, with a maximum deviation from literature values of approximately 0.80%.
By comparing the predicted human thermal signature to deblurred LWIR images and then fusing this information at the
feature level with high-resolution electro-optical image data, we can facilitate identity detection of objects in a scene
acquired under different conditions. Ultimately, our goal is to differentiate humans from their surroundings and label
non-human objects as thermal clutter.