近年來，鑑於更有效率且更友善的人機介面之需求漸漸提高，使得各種與人臉處理相關的研究迅速地成長。除了對人提供服務之外，最重要的是系統與人們的互動關係。在本論文中，我們提出一個具有人臉追蹤與辨識功能的系統，其中於人臉追蹤的部份，利用粒子濾除器技術對人臉區域作追蹤。由於我們考慮了頭髮的顏色，當人臉背對攝影機時，系統也能持續對其作正確的追蹤。另外，我們以運動與顏色兩者資訊當作特徵，使在追蹤的過程中儘可能讓背景的影響達到最低。而在人臉辨識的部份，我們提出了一個快速辨識的方式；此作法是在人臉偵測後，由小波轉換所得到的人臉影像特徵，交給 AdaBoost演算法所訓練出來的強分類器作判斷。與其它機器學習演算法相比較，這種 AdaBoost演算法在收斂的速度上佔了很大的優勢，因此可以時常更新我們的訓練樣本以應付不同的辨識對象，同時並不會花費太大的成本；最後，我們為多類別的人臉辨識發展一個由下而上的階層式分類架構。根據實驗結果顯示，我們所提方法的人臉追蹤率在一般的情況下高於 95%，而於人臉遭受暫時性遮蔽的情況下高於 88%，又人臉的辨識率經過統計可以在 90%以上，此外，整體系統的執行效能相當令人滿意，至少達到每秒20個畫面的速度。

Owing to the demand of more efficient and friendly human computer interface, the researches on face processing have been rapidly grown in recent years. In addition to providing some kinds of service for human beings, one of the most important characteristics of a system is to naturally interact with people. In this thesis, a design and experimental study of a face tracking and recognition system is presented. Regarding the face tracking, we utilize a particle filter to localize faces in image sequences. Since we have considered the hair color information of a human head, it will keep tracking even if the person is back to a camera. We further adopt both the motion and color cues as the features to make the influence of the background as low as possible. In the face recognition phase, a new architecture is proposed to achieve fast recognition. After the face detection process, we will capture the face region and fed its features derived from the wavelet transform into a strong classifier which is trained by an AdaBoost learning algorithm. Compared with other machine learning algorithms, the AdaBoost algorithm has an advantage of facilitating the speed of convergence. Thus, we can update the training samples to deal with comprehensive circumstances but need not spend much computational cost. Finally, we further develop a bottom-up hierarchical classification structure for multi-class face recognition. Experimental results reveal that the face tracking rate is more than 95% in general situations and 88% when the face suffering from temporal occlusion. As for the face recognition, the accurate rate is more than 90%; besides this, the efficiency of system execution is very satisfactory, which reaches 20 frames per second at least.

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