A comparative assessment of three approaches to pixel-level human skin-detection

This paper assesses the merits of three different approaches to pixel-level human skin detection. The basis for the 3 approaches has been reported in the literature. The first two approaches use simple ratios and colour space transforms respectively, whereas the third is a numerically efficient approach based on a 3D RGB probability map, first implemented by Rehg-Jones (1999). The Bayesian probabilities are made possible to compute only with the availability of a large appropriately labeled database. Over 12000 images from the Compaq skin and non-skin databases are used to quantitatively assess the three approaches. Thresholds are determined empirically to detect 95% of all skin-associated pixels and assessment is then made in terms of the percentage of non-skin pixels incorrectly accepted. The lowest of these false acceptance rates is found to be about 20% given by the 3D probability map.