A Real-Time Face Tracking using the Stereo Active Appearance Model

This paper proposes a real-time 3D face tracking system. This system uses a stereo active appearance model fitting (STAAM) algorithm that uses multiple calibrated perspective cameras to compute the 3D shape and rigid motion parameters. The use of calibration information reduces the number of model parameters, restricts the degree of freedom in the model parameters, and increases the accuracy and speed of fitting. The real-time face tracking system works alternating two different modes: the detection mode detects the face and eyes in an image and the tracking mode tracks the detected face using the STAAM. In addition, it utilizes a histogram matching to make the system robust to lighting conditions, and uses the motion information to compensate the temporal fluctuation of the captured images. The experimental results show that the proposed system operates robustly under varying lighting conditions and fast in the real-time manner at above 8 frames/sec.