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Faces and hands recorded under natural environments are frequently subject to illumination variations which affect their color appearance. This is a problem when the color cue is used to detect skin candidates at pixel level. Traditionally, color constancy has been suggested for correction, but after a lot of effort no good solution suitable for machine vision has emerged. However, many approaches have been proposed for general skin detection, but they are typically tested under mild changes in illumination chromaticity or do not define the variation range. This makes it difficult to evaluate their applicability for objects under varying illumination. The paper compares four state-of-the-art skin detection schemes under realistic conditions with drastic chromaticity change.