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Human face perception is currently an active research area in the computervision community. Skin detection is one of the most important and primarystages for this purpose. So far, many approaches are proposed to done thiscase. Near all of these methods have tried to find best match intensitydistribution with skin pixels based on popular color spaces such as RGB, HSI orYCBCR. Results show that these methods cannot provide an accurate approach forevery kind of skin. In this paper, an approach is proposed to solve thisproblem using a color probabilistic estimation technique. This approach isincluding two stages. In the first one, the skin intensity distribution isestimated using some train photos of pure skin, and at the second stage, theskin pixels are detected using Gaussian model and optimal threshold tuning.Then from the skin region facial features have been extracted to get the facefrom the skin region. In the results section, the proposed approach is appliedon FEI database and the accuracy rate reached 99.25%. The proposed approach canbe used for all kinds of skin using train stage which is the main advantageamong the other advantages, such as Low noise sensitivity and low computationalcomplexity.

A robust and adaptable method for face detection based on Color Probabilistic Estimation Technique