Performance evaluation of single and multiple-Gaussian models for skin color modeling

We present an experimental setup to evaluate the relative performance of single Gaussian models and Gaussian mixture models for skin color modeling. Firstly, a sample set of 1,120,000 skin pixels from a number of ethnic groups is selected and represented in the chromaticity space. Parameter estimation for both the single Gaussian and seven (with 2 to 8 Gaussian components) Gaussian mixture models is performed. For the mixture models, learning is carried out via the expectation-maximisation (EM) algorithm. In order to compare performances achieved by the 8 different models, we apply to each model a test set of 800 images - none from the training set. True skin regions, representing ground truth, are manually selected, and false positive and true positive rates are computed for each value of a specific threshold. Finally, receiver operating characteristics (ROC) curves are plotted for each model, making it possible to analyze and compare their relative performances. Results obtained show that, for medium to high true positive rates, mixture models (with 2 to 8 components) outperform the single Gaussian model. Nevertheless, for low false positive rates, all the models behave similarly.