Effect of colorspace transformation, the illuminance component, and color modeling on skin detection

Skin detection is an important preliminary process in human motion analysis. 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 of the absence of the illuminance component, and the two color modeling approaches are compared. The performance is measured by using a receiver operating characteristic (ROC) curve on a large dataset of 805 images 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 by transforming the pixel color to the SCT or HSI colorspaces, keeping the illuminance component, and modeling the color with the histogram approach.