This paper presents a comprehensive review and evaluation for color-based skin detection.The literature is reviewed in four categories: colorspace selection

skin color modeling

dynamic modeling and illumination invariance and adaptation.Using a large data set of 1 894 images

we examine whether the colorspace transformation can increase the compactness of skin distribution and the discriminability between skin and nonskin distributions in fourteen 3D colorspaces and fourteen 2D chrominance planes.We also evaluate the classification performance of skin probability map(SPM)

Gaussian mixture model(GMM)

self organizing map(SOM) and support vector machine(SVM) in said colorspaces.The results reveal that 1) the colorspace transformation cannot improve performance in general

the discriminability and testing performance in RGB and linear colorspaces are better than in other colorspaces

2) the Absence of the luminance component decreases discriminability and performance significantly

3) the performance of Bayes SPM in 3D colorspaces is superior to that of others

4) except 3D Bayes SPM

the 'color preference' of other detector is intrinsic and quite similar

5) the detector using skin and nonskin model simultaneously is better than the detector using skin model alone.