title: Classifying affective states using thermal infrared imaging of the human face.
abstract: In this paper, time, frequency, and time-frequency features derived from thermal infrared data are used to discriminate between self-reported affective states of an individual in response to visual stimuli drawn from the International Affective Pictures System. A total of six binary classification tasks were examined to distinguish baseline and affect states. Affect states were determined from subject-reported levels of arousal and valence. Mean adjusted accuracies of 70% to 80% were achieved for the baseline classifications tasks. Classification accuracies between high and low ratings of arousal and valence were between 50% and 60%, respectively. Our analysis showed that facial thermal infrared imaging data of baseline and other affective states may be separable. The results of this study suggest that classification of facial thermal infrared imaging data coupled with affect models can be used to provide information about an individual's affective state for potential use as a passive communication pathway.
authors.affiliation: Bloorview Research Institute and the Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M4G 1R8, Canada. brian.nhan@utoronto.ca
authors.forename: Brian R
authors.initials: BR
authors.lastname: Nhan
authors.forename: Tom
authors.initials: T
authors.lastname: Chau
mesh_terms: D000328:Adult; D000465:Algorithms; D000704:Analysis of Variance; D004644:Emotions / Q000145:classification* / Q000502:physiology; D005145:Face / Q000502:physiology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008957:Models, Genetic; D010363:Pattern Recognition, Automated / Q000379:methods*; D013817:Thermography / Q000379:methods*