Fast Face Tracking System

Facetracking is an important research field of computer vision and artificial intelligence. As a key technology, facetracking has received lots of attentions in automatic human face recognition, content based compression and retrieval, video conference, and other fields.Firstly, recent developments of facetracking and main algorithms of face detection & tracking are introduced in this thesis, and the Continuously Adaptive Mean Shift (CAMShift) algorithm is discussed in detail. Since the algorithm can robustly track target of different shape and size with the immunity against illuminant fluctuation and noise inference, and has low CPU load, it can serve as an efficient human and computer interface. However, CAMShift suffers when flesh-like interference and occlusion occur.Some methods, such as the enhanced color information, removing the flesh-like background and accessory information, are proposed to enhance the robust of CAMShift, and an AdaBoost fast face detector is used to initiate the searching window automatically. The improved CAMShhift algorithm can tra- ck an object robustly and automatically, which can track about 800 frames per second under the static background and can successfully resist the occlude and flesh-like interference. The experimental results demonstrate the robustness and efficiency of the proposed algorithm in real-time face tracking.The improved CAMShift algorithm is extended to multi-faces tracking. Same window elimination, maximum choice, and multiple accessory infor- mation methods are proposed to solve target-loss and individual identification problems. A whole frame PDI (Probabilistic Distribution Image) is produced to update the tracked objects. Empirical data have testified the proposed algor- ithm can fast (38~156fps), and accurately (above 97%) track multiple faces, where the tracking errors mainly happen in individual identification while the distance of two faces are too close and temporarily tracking failure when tra- cked face is occluded.