Probabilistic multiple face detection and tracking using entropy measures

A joint probabilistic face detection and tracking algorithm combining likelihood estimation and a prior probability is proposed in this paper. The likelihood estimation scheme is based on the statistical training of sets of automatically generated feature points and a mutual information tracking cue, while the prior probability estimation is based on a Gaussian temporal model. The likelihood estimation process is the core of a multiple face detection scheme used to initialize the tracking process. The resulting system was tested on real image sequences and is robust to significant partial occlusion and illumination changes. Index Terms--Arbitration scheme, illumination changes, mutual information, occlusion.