In this article, it makes the analysis discussion of the human skin detection. It detects the main characteristics of skin between non-skin both using RGB colors and chroma components (CbCr) ratios. By selecting a identification criterion, it can distinguish the skin pixels and non-skin pixels in a color image. At the beginning, the detection process will obtain a training image and it manually differentiates into two parts: skin and non-skin pixels. This discrimination process is called as global knowledge. After that different color ratios of these separated skin and non-skin pixels are calculated and their relationships between two parts are analyzed. The first method is using statistical histogram method to detect skin part in a color image. The histograms of different color ratios are derived from the new given image. By selecting proper thresholds, one can makes a best decision to determine whether one pixel is skin or not. The second method is using Backpropagation Neural Network (BPNN) to detect skin. A network is trained by using color ratios obtained above. Then the trained neural network is used to detect a new image and directly classifies the skin and non-skin parts from the given data. Finally the experimental results of two methods are shown and a comparison of two methods has been discussed. Both detection methods are effectively detected the skin pixels in our experiments.
