Probabilistic multiple face detection and tracking using entropy measures

Evangelos Loutas, Ioannis Pitas, Christophoros Nikou

Abstract: A joint probabilistic face detection and tracking algorithm, combining likelihood estimation and a prior probability, is proposed. 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 has been tested on real image sequences and is robust to significant partial occlusion and illumination changes.