Fast enhanced face-based adaptive skin color model

Abstract

Man machine interface by video analysis becomes popular recently. The most typical body gesture utilized for computer interaction is hand gesture. Therefore, it is a very important topic to accurately extract hand regions from a sequence of images in real time. In this paper, we propose an adaptive skin color model which is based on detected face color. Skin colors are sampled from extracted face region where non-skin color pixels like eyebrow or glasses are excluded. Gaussian distributions of normalized RGB are then used to define the skin color model for the detected person. To demonstrate the robustness of proposed model, experiments under diversified lighting and background are tested. Traditional methods based on RGB, Normalized RGB, and YCbCr are all implemented for comparison. From experimental results, skin color pixels could be detected for each person. The accuracy rate is 95.73% on average and is superior to previously mentioned methods.