Robust skin color segmentation using a 2D plane of RGB color space

This research features a new method for skin color segmentation using a 2D plane in the RGB color space. The RGB color values of the input color image do not need to be converted into HSI or YIQ color coordinates that have popularly been used for color segmentation. We have observed an important fact that skin colors in the RGB color space are approximately distributed in a linear fashion. Based on this fact, we have applied PCA (Principal Component Analysis) techniques to RGB values of skin colors from a set of training images. We detect skin regions by the lookup of skin color histogram computed based on a 2D color plane of which two axes correspond to two directions with smallest spread of skin colors. The proposed 2D color plane for color histogram lookup has an advantage over HS or IQ color planes. By using this plane, the problem of color constancy is much relieved. A learned color histogram contains most skin colors detected in the input images and at the same time, the distribution of skin colors in the plane is invariant compared to those in the HS or IQ planes. We have evaluated the performance of the proposed method by comparing with the performance of color histogram lookup methods based on HS or IQ color plane. The experimental results show that the performance of our method is robust to illumination changes.