  Estimation of facial expressions, as spatio-temporal processes, can take
advantage of kernel methods if one considers facial landmark positions and
their motion in 3D space. We applied support vector classification with kernels
derived from dynamic time-warping similarity measures. We achieved over 99%
accuracy - measured by area under ROC curve - using only the 'motion pattern'
of the PCA compressed representation of the marker point vector, the so-called
shape parameters. Beyond the classification of full motion patterns, several
expressions were recognized with over 90% accuracy in as few as 5-6 frames from
their onset, about 200 milliseconds.
