On Selecting an Appropriate Colour Space for Skin Detection

We present a comprehensive and systematic approach for skin detection. We have evaluated each component of several colour models, and then we selected a suitable colour model for skin detection. Such approach is well-known in the machine learning community as attribute selection. After listing the top components, we exemplify that a mixure of colour components can discriminate very well skin in both indoor and outdoor scenes. The spawning space created by such componens is nearly convex, therefore it allow us to use even simple rules to discriminate skin to non-skin points. These simple rules can recognise 96% of skin points with just 11% of false positives. This is a data analysis approach that will help to many skin detection systems