Unsupervised and adaptive Gaussian skin-color model.

Bergasa, Luis M.; Mazo, M.; Gardel, Alfredo; Sotelo, Miguel Ángel; Boquete, Luciano
Year: 2000
Type of Publication: Article
Keywords: dblp
Journal: Image Vision Comput.
Volume: 18
Number: 12
Pages: 987-1003
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.
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