Our objective is to track multiple faces in a video sequence. We combine a frame-based face detection algorithm based on a coarse-to-fine search with a dynamic Markov model for the joint evolution of poses and observations. The link between the two is the trace model, a probability distribution on the global history of online processing during static face detection. The trace is our frame-based observation and its conditional distribution, given the states of interest (namely multiple face poses and “background”), serves as our likelihood functionP (observation|state). Consequently, the resulting framework unites face detection and face tracking in a single probabilistic setting. Due to continuously updating detections, there are no prior restrictions on face movements. The tracker is tested on real image sequences and accommodates large movements, partial occlusions, varying numbers of faces, and changes in illumination.
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