Skin detection using spatial analysis with adaptive seed
This paper introduces a new method for adaptive skin detection in color images combined with spatial analysis of skin pixels. It has been reported in many works that adaptation of a skin color model to a particular image may decrease the false positives, however the false negatives are considerably high unless a local model is combined with the global one. Another possibility for improvement is to analyze spatial properties of the pixels classified as skin, but this operation strongly depends on the seed extraction technique. Our contribution lies in using a local dynamic skin model learned from the detected faces to extract seeds for the spatial analysis. We present an extensive experimental study confirming that our method outperforms alternative skin detection techniques.