Face detection based on a new nonlinear color space

Abstract

Color is a useful piece of information in computer vision especially for skin detection. In this paper, we propose a novel approach for skin segmentation and facial feature extraction. The proposed skin segmentation is a method for integrating the chrominance components of nonlinear YCrCb color model. The chrominance components of nonlinear YCrCb color space were modeled using a subgaussian probability density function, and then the face skin was segmented based on this function. In order to autheticate the face candidates region, firstly texture information in face candidate regions would be segmented using mean and variance of luminance information, and then eye would be located by the PCA edge direction information, and finally, the others features, such as nose and mouth, also were detected using the geometrical shape information. As all the above-mentioned techniques are simple and efficient, the proposed skin segmentation based on nonlinear color space method is invariably of lighting and pose. In our experiments, the proposed method has been successfully evaluated using two different test datasets. The detection accuracy is around 98%, the average run time ranged from 0.1-0.3 sec per frame.