Unsupervised and adaptive Gaussian skin-color model

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 clus...

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Bibliographic Details
Published in:Image and vision computing 2000-09, Vol.18 (12), p.987-1003
Main Authors: Bergasa, L.M., Mazo, M., Gardel, A., Sotelo, M.A., Boquete, L.
Format: Article
Language:English
Subjects:
Bayesian methods
Color clustering
Gaussian mixture models
Skin segmentation
Unsupervised learning
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Summary: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.
ISSN:0262-8856
1872-8138
DOI:10.1016/S0262-8856(00)00042-1