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.
['Zhanyu Ma', 'Arne Leijon']
Human skin color detection in RGB space with Bayesian estimation of beta mixture models