A probabilistic model for the human skin color

We present a multivariate statistical model to represent the human skin color. There are no limitations regarding whether the person is white or black, once the model is able to learn automatically the ethnicity of the person involved. We propose to model the skin color in the chromatic subspace, which is by default normalized with respect to illumination. First, skin samples from both white and black people are collected. These samples are then used to estimate a parametric statistical model, which consists of a mixture of Gaussian probability density functions (pdfs). Estimation is performed by a learning process based on the expectation-maximization (EM) algorithm. Experiments are carried out and receiver operating characteristics (ROC curves) are obtained to analyse the performance of the estimated model. The results are compared to those of models that use a single Gaussian density.