• University: 1Institute for Neural Computation, University of California, San Diego, 2Department of Communication, University at Buffalo, State
  • Authors: Marian Stewart Bartlett1, Gwen C. Littlewort1, Mark G. Frank2, Claudia Lainscsek1, Ian R. Fasel1, Javier R. Movellan1


Spontaneous facial expressions differ from posed expressions in both which muscles are moved, and in the dynamics of the movement. Advances in the field of automatic facial expression measurement will require development and assessment on spontaneous behavior. Here we present preliminary results on a task of facial action detection in spontaneous facial expressions. We employ a user indepen- dent fully automatic system for real time recognition of facial actions from the Facial Action Coding System (FACS). The system automatically detects frontal faces in the video stream and coded each frame with respect to 20 Action units. The approach applies machine learning methods such as support vector machines and AdaBoost, to texture-based image representations. The output margin for the learned classifiers predicts action unit intensity. Frame-by-frame intensity measurements will enable investigations into facial expression dynamics which were previously intractable by human coding.

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