Skin color segmentation based on improved 2-D Otsu and YCgCr color space

In order to deal with the disadvantages of fixed-threshold method in skin color segmentation, a new algorithm based on 2-D Otsu and YCgCr color space was proposed after analysis and comparison of different color spaces and skin color models. Firstly, the skin color sample images compensated with light were transferred from RGB to YCgCr color space and the 2-D Gaussian skin color model was established based on the 179221 skin pixels. Secondly, the image to be segmented was light compensated and transferred from RGB to YCgCr color space. Thirdly, skin color similarity degree was computed based on the 2-D Gaussian model and the skin color similarity image was obtained. Finally, an improved 2-D Otsu method was used into segmentation of the skin color similarity image. Theoretical analysis and experimental results show that the new skin color segmentation method is superior to the traditional methods based on a fixed-threshold in pertinence and anti-noise robustness.