本論文使用Adaboost演算法為基礎，開發一套混合式人臉檢測系統。並且在本論文當中提出兩個新的基於像素導向的演算法，一為階層式特徵；另一為統計式遮罩，並且搭配使用Haar-like特徵之強分類器，所建構出一混合式人臉檢測系統。利用此混合式人臉檢測系統搭配PTZ類比攝影機，成功開發出一套完整人臉偵測以及追蹤系統。
人臉偵測一直以來為學術界相當感興趣的主題之一，目前已發展出多種成熟的人臉偵測技術。但如何維持高人臉偵測率的同時也達到幾近於零的錯誤警報率，並且能夠以低複雜度的演算法實現高效率即時檢測系統，一直以來為人臉偵測此議題最大的目標。本論文嘗試以統計的方式，觀察在一24x24的子影像當中，每一像素點的分佈情形，並且利用具有相當指標性意義的標準差數值建構出一人臉遮罩，搭配最小均方法(Least Mean Square，LMS)調整遮罩權重；另外，一階層式特徵在本論文被提出，希望能以具有低複雜度並且同時有良好的分類能力的階層式特徵，來做一排除非人臉以及保留人臉之重要的子系統，最後並以串接(Cascade)的方式將本論文提出之兩個方法與知名的Haar-like特徵所組成之強分類器構造出一混合式系統，實驗證明以本論文構造出之人臉偵測系統對於正面人臉具有良好之偵測能力。
最後，本研究也提出此混合式人臉偵測系統之實際應用，使用一PTZ類比攝影機，並且利用USB影像擷取卡轉換類比影像，再搭配筆記型電腦組成一人臉偵測以及追蹤系統。我們利用PTZ攝影機可左右各170度旋轉、向上90度、向下30度以及18倍光學變焦特性，成功以Visual C++ 6.0開發出一人臉追蹤系統，證明以本論文所提出之演算法有其實用之價值。

In this paper, we proposed two novel algorithms for face detection. One is Pixel-Based Hierarchical Feature (PBHF) and the other one is Statistics-Based Face Mask (SBFM). Furthermore, we made a combination of cascade system of PBHF, SBFM and a strong classifier with Haar-like feature. Finally, we use the combined system and a PTZ camera to develop the face detection and tracking system.
In the past, some excellent face detection algorithms are proposed by researcher. These algorithms try to achieve three goals that are maintaining high face detection rate, extremely low false alarm rate and real-time detecting. In this paper, we try to make a Statistics mask and using LMS algorithm to adjust weights of mask. Furthermore, we try to use a low-complexity hierarchical feature to help filter out non-face sub-windows and remain face sub-windows. Experience results prove our method to have a fine classifier ability for frontal face detecting.
Finally, we proposed a application for our method, to use a PTZ camera implement a face detection and tracking system. Some characteristics of PTZ are that right and left 170 rotations, 90 upward, 30 downward. We successfully developed an face detection and tracking system. Prove the applied value of our method.

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