Adaptive skin pixel classification technique based on hybrid color spaces

Ramya Reddy Maaram, Satyanadh Gundimada, K. Vijayan Asari

Abstract: An adaptive skin segmentation algorithm robust to illumination changes and skin like backgrounds is presented in this paper. Skin pixel classification has been limited to only individual color spaces. There has not been a comprehensive evaluation of which color components or a combination of color components would provide the best skin pixel classification. Although the R, G, B components are the three primary features, transformation of these components to different color spaces provide additional set of features. The color components or the features present within a single color space may not be the best when it comes to skin pixel classification. In this paper an adaboost based skin segmentation technique is presented. Bayesian classifiers trained on the skin and non-skin probability densities specific color component spaces form the set of weak classifiers which adaboost is implemented. Additional classifiers are generated by varying the associated thresholds of the Bayesian classifiers. in An adaptive image enhancement technique is implemented to improve the illumination as well as the color of an image. This will enable to identify the skin pixels more accurately in the presence of non-uniform lighting conditions. Human skin texture is fairly uniform. This property is utilized to develop a method, which is based on the neighborhood information of a pixel. This step will provide more information in addition to color about a pixel being skin or non-skin. A comparison of the existing color based and neighborhood methods with the proposed technique is presented in this paper.