Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)
G.C. Littlewort, M.S. Bartlett, I.R. Fasel, J. Chenu, T. Kanda, H. Ishiguro, J.R. Movellan
Computer animated agents and robots bring a social dimension to hu- man computer interaction and force us to think in new ways about how computers could be used in daily life. Face to face communication is a real-time process operating at a time scale of less than a second. In this paper we present progress on a perceptual primitive to automatically detect frontal faces in the video stream and code them with respect to 7 dimensions in real time: neutral, anger, disgust, fear, joy, sadness, sur- prise. The face ﬁnder employs a cascade of feature detectors trained with boosting techniques [13, 2]. The expression recognizer employs a novel combination of Adaboost and SVM’s. The generalization performance to new subjects for a 7-way forced choice was 93.3% and 97% correct on two publicly available datasets. The outputs of the classiﬁer change smoothly as a function of time, providing a potentially valuable repre- sentation to code facial expression dynamics in a fully automatic and unobtrusive manner. The system was deployed and evaluated for mea- suring spontaneous facial expressions in the ﬁeld in an application for automatic assessment of human-robot interaction.