The automated detection and tracking of humans in computer vision necessitates improved modeling of the human skin appearance. We propose a Bayesian network approach for skin detection. We test several classifiers and propose a methodology for incorporating unlabeled data. We apply the semi-supervised approach to skin detection and we show that learning the structure of Bayesian network classifiers enables learning good classifiers with a small labeled set and a large unlabeled set.