Research on a Skin Color Detection Algorithm Based on Self-adaptive Skin Color Model

Abstract

Skin color detection is an important subject in computer vision research. Color segmentation takes a great attention because color is an effective and robust visual cue for characterizing an object from the others. To aim at existing skin color algorithms considering the luminance information not enough, a reliable color modeling approach was proposed. It is based on the fact that color distribution of a single-colored object is not invariant with respect to luminance variations even in the Cb-Cr plane and does not ignore the influence on luminance Y component in YCbCr color space. Firstly, according to statistics of skin color pixels, we take the luminance Y by ascending order, divide the total range of Y into finite number of intervals, collect pixels whose luminance belongs to the same luminance interval, calculate the covariance and the mean value of Cb and Cr with respect to Y, and use the above data to train the BP neural network, then we get the self-adaptive skin color model and design a Gaussian model classifier. The experimental results have indicated that this algorithm can effectively fulfill the skin-color detection for images captured under different environmental condition and the performance of the skin color segmentation is significantly improved.