Human skin color detection in RGB space with Bayesian estimation of beta mixture models

Human skin color detection plays an important role in the applications of skin segmentation, face recognition, and tracking. To build a robust human skin color classifier is an essential step. This paper presents a classifier based on beta mixture models (BMM), which uses the pixel values in RGB space as the features. We propose a Bayesian estimation method based on the variational inference framework to approximate the posterior distribution of the parameters in the BMM and take the posterior mean as a point estimate of the parameters. The well-known Compaq image database is used to evaluate the performance of our BMM based classifier. Compared to some other skin color detection methods, our BMM based classifier shows a better recognition performance.