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Human skin color is a powerful fundamental cue that can be used in particular, at an early stage, for the important applications of face and hand detection in color images"2, and ultimately, for meaningful human-computer interactions.In this paper, we analyze the distribution of human skin for a large number of three-dimensional (3-D) color spaces (or 2-D chrominance spaces) and for skin images recorded with two different camera systems. By use of seven different criteria, we show that mainly the normalized r-g and CIE-xy chrominance spaces, or spaces_constructed as a suitable linear combination or as ratios of normalized r, g and b values, or a space normalized by '1R2 +C + B2, are consistently the most efficient for skin pixel detection and consequently, for image segmentation based on skin color. In particular, in these spaces the skin distribution can be modeled by a simple, single elliptical Gaussian, and it is most robust to a change of camera system.

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