Geintra

Departamento de electronica Universidad de Alcala

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    UNSUPERVISED AND ADAPTIVE GAUSSIAN SKIN-COLOR MODEL

    TitleUNSUPERVISED AND ADAPTIVE GAUSSIAN SKIN-COLOR MODEL
    Publication TypeJournal Article
    Año de publicación2000
    AutoresBergasa, LM, Mazo, M, Gardel, A, Sotelo, MA, Boquete, L
    Idioma de publicaciónEnglish
    JournalImage and Vision Computing
    Volumen18
    Número12
    Páginas987-1003
    Fecha de publicación03/2000
    EditorialElsevier
    JCR Impact Factor0.900
    ISSN0262-8856
    URLwww.robesafe.com/personal/sotelo/ImageVisionComputing2000.pdf
    DOIdoi:10.1016/S0262-8856(00)00042-1
    Abstract

    In this article a segmentation method is described for the face skin of people of any race in real time, in an adaptive and unsupervised way, based on a Gaussian model of the skin color (that will be referred to as Unsupervised and Adaptive Gaussian Skin-Color Model, UAGM). It is initialized by clustering and it is not required that the user introduces any initial parameters. It works with complex color images, with random backgrounds and it is robust to lighting and background changes. The clustering method used, based on the Vector Quantization (VQ) algorithm, is compared to other optimum model selection methods, based on the EM algorithm, using synthetic data. Finally, real results of the proposed method and conclusions are shown.