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<p/> <p>It is challenging to track multiple facial features simultaneously when rich expressions are presented on a face. We propose a two-step solution. In the first step, several independent condensation-style particle filters are utilized to track each facial feature in the temporal domain. Particle filters are very effective for visual tracking problems; however multiple independent trackers ignore the spatial constraints and the natural relationships among facial features. In the second step, we use Bayesian inference&#8212;belief propagation&#8212;to infer each facial feature's contour in the spatial domain, in which we learn the relationships among contours of facial features beforehand with the help of a large facial expression database. The experimental results show that our algorithm can robustly track multiple facial features simultaneously, while there are large interframe motions with expression changes.</p>