Maximum Entropy Models for Skin Detection

We consider a sequence of three models for skin detection built from a large collection of labelled images. Each model is a maximum entropy model with respect to constraints concerning marginal distributions. Our models are nested. The first model, called the baseline model is well known from practitioners. Pixels are considered as independent. Performance, measured by the ROC curve on the Compaq Database is impressive for such a simple model. However, single image examination reveals very irregular results. The second model is a Hidden Markov Model which includes constraints that force smoothness of the solution. The ROC curve obtained shows better performance than the baseline model. Finally, color gradient is included. Thanks to Bethe tree approximation, we obtain a simple analytical expression for the coefficients of the associated maximum entropy model. Performance, compared with previous model is once more improved.