1. Intro

• FER by RGB
  • Superficial facial features laying on the epidermis
  • stress, fatigue, etc cannot be captured by RGB
• FER by Thermal image
  • temperature → vital signs
• present the state of the literature and discuss the challenges hindering the full adoption of AI as a tool for thermal FER.

2. Advantage of thermal

• long-wave Infrared Radiation (LWIR) used alone, as a data source for FER,
  • provide some form of anonymity for healthcare applications to minimize racial, ethnic, and potentially gender bias, when compared to RGB for FER

1. invariant to lighting conditions unlike RGB, allowing the detection of physiological response (heat) to occur in low light or total darkness

2. reliable and accurate correlation to standard physiological measures like respiration and heart rate

3. is non-invasive i.e., requiring no skin contact whatsoever, making it convenient and non-intrusive and potentially relevant for non communicative persons

4. resistant to intentional deceit since physiological responses cannot be faked, whereas visible facial expressions can be controlled

5. reveal facial disguises (i.e. wigs, masks)

   • since these materials have high reflectivity and display as the brightest on thermograms compared to human skin which is among the darkest objects with low reflectivity

2. Thermal FER Datasets

3. Thermal FER Papers

4. Thermal FER Experimental Design Parameters