  Skin detection is one of the most important and primary stages in some of
image processing applications such as face detection and human tracking. So
far, many approaches are proposed to done this case. Near all of these methods
have tried to find best match intensity distribution with skin pixels based on
popular color spaces such as RGB, CMYK or YCbCr. Results show these methods
cannot provide an accurate approach for every kinds of skin. In this paper, an
approach is proposed to solve this problem using statistical features
technique. This approach is including two stages. In the first one, from pure
skin statistical features were extracted and at the second stage, the skin
pixels are detected using HSV and YCbCr color spaces. In the result part, the
proposed approach is applied on FEI database and the accuracy rate reached
99.25 + 0.2. Further proposed method is applied on complex background database
and accuracy rate obtained 95.40+0.31%. The proposed approach can be used for
all kinds of skin using train stage which is the main advantages of it. Low
noise sensitivity and low computational complexity are some of other
advantages.
