A computer user's facial displays will be context dependent, especially in the presence of an embodied agent. Furthermore, each interactant will use their face in different ways, for different purposes. These two hypotheses motivate a method for clustering patterns of motion in the human face. Facial motion is described using optical flow over the entire face, projected to the complete orthogonal basis of Zernike polynomials. A context-dependent mixture of hidden Markov models (cmHMM) clusters the resulting temporal sequences of feature vectors into facial display classes. We apply the clustering technique to sequences of continuous video, in which a single face is tracked and spatially segmented. We discuss the classes of patterns uncovered for a number of subjects.
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