Abstract
Machines would require the ability to perceive and adapt to affects for achieving artificial sociability. Most autonomous systems use Automated Facial Expression Classification (AFEC) and Automated Affect Interpretation (AAI) to achieve sociability. Varying lighting conditions, occlusion, and control over physiognomy can influence the real life performance of vision-based AFEC systems. Physiological signals provide complementary information for AFEC and AAI. We employed transient facial thermal features for AFEC and AAI. Infrared thermal images with participants' normal expression and intentional expressions of happiness, sadness, disgust, and fear were captured. Facial points that undergo significant thermal changes with a change in expression termed as Facial Thermal Feature Points (FTFPs) were identified. Discriminant analysis was invoked on principal components derived from the Thermal Intensity Values (TIVs) recorded at the FTFPs. The cross-validation and person-independent classification respectively resulted in 66.28% and 56.0% success rates. Classification significance tests suggest that (1) like other physiological cues, facial skin temperature also provides useful information about affective states and their facial expression; (2) patterns of facial skin temperature variation can complement other cues for AFEC and AAI; and (3) infrared thermal imaging may help achieve artificial sociability in robots and autonomous systems.