Real-time classification of evoked emotions using facial feature tracking and physiological responses

We present automated, real-time models built with machine learning algorithms which use videotapes of subjects' faces in conjunction with physiological measurements to predict rated emotion (trained coders' second-by-second assessments of sadness or amusement). Input consisted of videotapes of 41 subjects watching emotionally evocative films along with measures of their cardiovascular activity, somatic activity, and electrodermal responding. We built algorithms based on extracted points from the subjects' faces as well as their physiological responses. Strengths of the current approach are (1) we are assessing real behavior of subjects watching emotional videos instead of actors making facial poses, (2) the training data allow us to predict both emotion type (amusement versus sadness) as well as the intensity level of each emotion, (3) we provide a direct comparison between person-specific, gender-specific, and general models. Results demonstrated good fits for the mode...

• Type: Journal
• Year: 2008
• Where: IJMMS
• Authors: Jeremy N. Bailenson, Emmanuel D. Pontikakis, Iris B. Mauss, James J. Gross, Maria E. Jabon, Cendri A. C. Hutcherson, Clifford Nass, Oliver John
• Keywords: Emotion, General Models, IJMMS 2008, Physiological