Objective evaluation of approaches of skin detection using ROC analysis

Skin detection is an important indicator of human presence and actions in many domains, including interaction, interfaces and security. It is commonly performed in three steps: transforming the pixel color to a non-RGB colorspace, dropping the illuminance component of skin color, and classifying by modeling the skin color distribution. In this paper, we evaluate the effect of these three steps on the skin detection performance. The importance of this study is a new comprehensive colorspace and color modeling testing methodology that would allow for making the best choices for skin detection. Combinations of nine colorspaces, the presence or the absence of the illuminance component, and the two color modeling approaches are compared for different settings (indoor or outdoor) and modeling parameters (the histogram size). The performance is measured by using a receiver operating characteristic (ROC) curve on a large dataset of 845 images (consisting more than 18.6 million pixels) with manual ground truth. The results reveal that (1) colorspace transformations can improve performance in certain instances, (2) the absence of the illuminance component decreases performance, and (3) skin color modeling has a greater impact than colorspace transformation. We found that the best performance was obtained on indoor images by transforming the pixel color to the HSI or SCT colorspaces, keeping the illuminance component, and modeling the color with the histogram approach using a larger size distribution.