A survey of skin-color modeling and detection methods
Pattern Recognition
Saliency model-based face segmentation and tracking in head-and-shoulder video sequences
Journal of Visual Communication and Image Representation
Generalized Gaussian density for skin detection in DCT domai
Machine Graphics & Vision International Journal
A skin detection approach based on the Dempster--Shafer theory of evidence
International Journal of Approximate Reasoning
Computational strategies for skin detection
CCIW'13 Proceedings of the 4th international conference on Computational Color Imaging
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We present an experimental setup to evaluate the relative peiformance of single gaussian and mixture of gaussians models for skin color modeling. Firstly, a sample set of J, J 20, 000 skin pixels from a number of ethnic groups is selected and represented in the chromaticity space. In the following, parameter estimation for both the single gaussian and seven (with 2 to 8 gaussian components) gaussian mixture models is peiformed. For the mixture models, learningis 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 800images -none from the training set. 1rue skin regions, representing the 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, which make 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.