Abstract
Detection and classification of skin regions plays important roles in many image processing and vision applications. In this paper, we present a statistical approach for fast skin detection in MPEG-compressed videos. Firstly, conditional probabilities of skin and non-skin pixels are extracted from manual marked training images. Then, candidate skin pixels are identified using the Bayesian maximum a posteriori decision rule. An optimal threshold is then obtained by analyzing of probability error on the basis of the likelihood ratio histogram of skin and non-skin pixels. Experiments from sequences with varying illuminations have demonstrated the effectiveness of our approach. © Springer-Verlag Berlin Heidelberg 2007.