Abstract
ABSTRACT Man machine interface by video analysis becomes popular recently. The most frequently adopted body parts for computer interaction are face and hands. Therefore, it is a very important topic to accurately extract face and hand regions from a sequence of images in real time. In this paper, we propose an adaptive skin color model which is based on face detection. Skin colors were sampled from extracted face region where non-skin color pixels like eyebrow or eyeglasses could be excluded. Gaussian distributions of normalized RGB were 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 were tested. Traditional methods based on RGB, Normalized RGB, and YCbCr were 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.
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