On-premise skin color modeing method for vision-based hand tracking

We propose a vision-based method that robustly extracts hands from backgrounds irrespective of illumination conditions. Many hand tracking systems build a skin color model before the system runs and then they track hands by using the color model. However, the system is unstable because the pre-defined colors cannot be adapted to various illuminations and human skin colors. To circumvent the problem, we postpone the hand color modeling task until the system runs. The system can verify whether an object is a hand or not, and the verified hand is used for modeling a skin color. The method is effective not only for accurate hand extraction but also for reducing noises of backgrounds because the skin color model is optimized to the user's hand and the current illumination. The experiment result shows that the method dramatically improves accuracies of hand tracking and gesture recognition.