Learning skin distribution using a sparse map

We present a new skin modeling technique based on SNoW (sparse network of Winnows) for accurate and robust skin region detection. A skin distribution map (SDM) representing the sparse network is trained with skin pixels to learn their distribution in a color space. We then train the SDM with non-skin pixels to unlearn the distribution of the non-skin pixels, which overlap with the skin pixels in the color space. This skin model can be used for skin detection on any color space. We have found the accuracy of skin detection using SDM to be slightly better than that using the skin probability map (SPM) method. The main advantage of using the SDM method over the SPM method is that the complexity, memory requirements and time for skin detection are reduced significantly.