@InProceedings{CaetanoOlabBaro:2002:PeEvSi,
               author = "Caetano, Tib{\'e}rio S. and Olabarriaga, S{\'{\i}}lvia D. and 
                         Barone, Dante A. C.",
                title = "Performance evaluation of single and multiple-gaussian models for 
                         skin color modeling",
            booktitle = "Proceedings...",
                 year = "2002",
               editor = "Gon{\c{c}}alves, Luiz Marcos Garcia and Musse, Soraia Raupp and 
                         Comba, Jo{\~a}o Luiz Dihl and Giraldi, Gilson and Dreux, 
                         Marcelo",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 15. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
                 note = "The conference was held in Fortaleza, CE, Brazil, from October 7 
                         to 10.",
             abstract = "We present an experimental setup to evaluate the relative 
                         performance of single gaussian and mixture of gaussians 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. In the following, 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 
                         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.",
  conference-location = "Fortaleza, CE, Brazil",
      conference-year = "10-10 Oct. 2002",
                  doi = "10.1109/SIBGRA.2002.1167155",
                  url = "http://dx.doi.org/10.1109/SIBGRA.2002.1167155",
             language = "en",
         organisation = "SBC - Brazilian Computer Society",
                  ibi = "6qtX3pFwXQZeBBx/vRS9M",
                  url = "http://urlib.net/ibi/6qtX3pFwXQZeBBx/vRS9M",
           targetfile = "69.pdf",
        urlaccessdate = "2024, May 02"
}