The face provides a variety of different communicative functions such as identification, the perception of emotional expressions, and lip-reading. Many applications in human computer interaction require tracking a human face. Tracking human faces is one of our efforts of user modeling which is to provide the computer with necessary information about users and environment.

We have developed a system that can track a person's face while the person moves freely (walks, jumps, sits and rises). The system has achieved a rate of up to 30+ frame/second using a low end workstation (HP9000) with a framegrabber and a Canon VC-C1 camera. Three types of models have been employed in developing the system.

First, we have proposed a stochastic model to characterize skin colors of human faces. The information provided by the model is sufficient for tracking a human face in various poses and views. This model can adapt in real-time to different people and different lighting conditions.
Then, a motion model is used to estimate image motion and to predict search window. Third, a camera model is used to predict and to compensate for camera motion. The system has been demonstrated to hundreds of people, and tested by different inputs (video cameras, video tape, and TV news) and under different environments (indoor and outdoor).

Using the skin color model, we can find the face region:

If we analyze the face region:

We can discover the skin color distribution clusters in a small area of the chromatic color space:

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