A comparison of thermal image descriptors for face analysis

Thermal imaging is a type of imaging that uses thermographic cameras to detect radiation in the infrared range of the electromagnetic spectrum. Thermal images are particularly well suited for face detection and recognition because of the low sensitivity to illumination changes, color skins, beards and other artifacts. In this paper, we take a fresh look at the problem of face analysis in the thermal domain. We consider several thermal image descriptors and assess their performance in two popular tasks: face recognition and facial expression recognition. The results have shown that face recognition can reach accuracy levels of 91% with Localized Binary Patterns. Also, despite the difficulty of facial expression detection, our experiments have revealed that Haar based features (FCTH - Fuzzy Color and Texture Histogram) offers the best results for some facial expressions.