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Author s personal copy ARTICLE IN PRESS Int J Human Computer Studies 66 2008 303 317 www elsevier com locate ijhcs Real time classi cation of evoked emotions using facial feature tracking and physiological responses Jeremy N Bailensona Emmanuel D Pontikakisb Iris B Maussc James J Grossd Maria E Jabone Cendri A C Hutchersond Clifford Nassa Oliver Johnf a Department of Communication Stanford University Stanford CA 94305 USA Department of Computer Science Stanford University Stanford CA 94305 USA c Department of Psychology 2155 South Race Street University of Denver Denver CO 80208 USA d Department of Psychology Stanford University Stanford CA 94305 USA e Department of Electrical Engineering Stanford University Stanford CA 94305 USA f Department of Psychology University of California Berkeley CA 94720 USA b Received 15 February 2007 received in revised form 28 October 2007 accepted 29 October 2007 Communicated by S Brave Available online 1 November 2007 Abstract 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 lms 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 speci c gender speci c and general models Results demonstrated good ts for the models overall with better performance for emotion categories than for

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