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With the increased attention on thermal imagery for Covid-19 screening, thepublic sector may believe there are new opportunities to exploit thermal as amodality for computer vision and AI. Thermal physiology research has beenongoing since the late nineties. This research lies at the intersections ofmedicine, psychology, machine learning, optics, and affective computing. Wewill review the known factors of thermal vs. RGB imaging for facial emotionrecognition. But we also propose that thermal imagery may provide asemi-anonymous modality for computer vision, over RGB, which has been plaguedby misuse in facial recognition. However, the transition to adopting thermalimagery as a source for any human-centered AI task is not easy and relies onthe availability of high fidelity data sources across multiple demographics andthorough validation. This paper takes the reader on a short review of machinelearning in thermal FER and the limitations of collecting and developingthermal FER data for AI training. Our motivation is to provide an introductoryoverview into recent advances for thermal FER and stimulate conversation aboutthe limitations in current datasets.

The Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Data