Tracking facial markers with an adaptive marker collocation model

The paper presents a robust and computationally low-cost technique for tracking facial markers which exploits an adaptive marker collocation model to recover from tracking errors. Marker collocation statistics are estimated during periods where the markers are successfully tracked, and employed to estimate the position of missing markers during periods where the tracker fails to locate the full set. Evaluation experiments have been conducted on a small audio-visual corpus of connected digits in which the speaker was recorded with small white markers affixed to easily locatable points on the chin, lips, and nose. It is demonstrated that use of the marker collocation model makes the tracker robust in the face of marker occlusion.