Automated classification and recognition of facial expressions using infrared thermal imaging

Facial expressions classification (FEC) software has usually been based upon the analysis of visible-spectrum images. Little work has been done on the use of infrared thermal imaging (IRTI) in this area. We report ongoing work on the use of IRTI for FEC. We have identified thermally significant points on human faces, termed facial thermal feature points (FTFPs) and have discovered that variances in thermal intensity values (TIVs) recorded at these FTFPs can help classify common intentional facial expressions. Using multivariate tests and linear discriminant analysis, we examined whether it is possible to distinguish between faces on the basis of TIVs for FEC. Results show that TIVs provide a viable set of thermal data that can be used to classify intentional facial expressions of happiness, sadness and disgust. IRTI may provide an alternative, or be complementary, to visible-spectrum based FEC techniques. IRTI also promises nonintrusive facial feature extraction and FEC in low illumination and image quality conditions.