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

被引:168
作者
Bailenson, Jeremy N. [1 ]
Pontikakis, Emmanuel D. [2 ]
Mauss, Iris B. [3 ]
Gross, James J. [4 ]
Jabon, Maria E. [5 ]
Hutcherson, Cendri A. C. [4 ]
Nass, Clifford [1 ]
John, Oliver [6 ]
机构
[1] Stanford Univ, Dept Commun, Stanford, CA 94305 USA
[2] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA
[3] Univ Denver, Dept Psychol, Denver, CO 80208 USA
[4] Stanford Univ, Dept Psychol, Stanford, CA 94305 USA
[5] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA
[6] Univ Calif Berkeley, Dept Psychol, Berkeley, CA 94720 USA
基金
美国国家科学基金会;
关键词
affective computing; facial tracking; emotion; computer vision;
D O I
10.1016/j.ijhcs.2007.10.011
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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 models overall, with better performance for emotion categories than for emotion intensity, for amusement ratings than sadness ratings, for a full model using both physiological measures and facial tracking than for either cue alone, and for person-specific models than for gender-specific or general models. (c) 2007 Elsevier Ltd. All rights reserved.
引用
收藏
页码:303 / 317
页数:15
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