  Human face perception is currently an active research area in the computer
vision community. Skin detection is one of the most important and primary
stages for this purpose. 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, HSI or
YCBCR. Results show that these methods cannot provide an accurate approach for
every kind of skin. In this paper, an approach is proposed to solve this
problem using a color probabilistic estimation technique. This approach is
including two stages. In the first one, the skin intensity distribution is
estimated using some train photos of pure skin, and at the second stage, the
skin pixels are detected using Gaussian model and optimal threshold tuning.
Then from the skin region facial features have been extracted to get the face
from the skin region. In the results section, the proposed approach is applied
on FEI database and the accuracy rate reached 99.25%. The proposed approach can
be used for all kinds of skin using train stage which is the main advantage
among the other advantages, such as Low noise sensitivity and low computational
complexity.
