在快速人臉偵測的研究中，Viola和Jones提出了一個連接式的架構，此架構能得到高辨識率及低錯誤率；他們使用integral image來計算人臉特徵值。本研究提出了兩種新的integral image: triangle integral image以及各別對應的三角特徵。另外，本研究以Discrete AdaBoost為基礎，提出了一個能在訓練時降低非人臉錯誤率的方法。我們的實驗證明，三角特徵能使得需要的feature數減少；改進過後的AdaBoost能使得錯誤率更低。

Viola and Jones introduce a fast face detection system which uses a cascaded structure that can achieve high detection rate and low false positive rate. Their system uses integral images to compute values from features. This thesis introduces two new types of integral images which are called triangle integral images and two corresponding features which are named triangle features. And this thesis proposes a method to lower training error by modifying Discrete AdaBoost. As results, to use triangle features can decrease the numbers of features; this research achieves lower false positive rate and fewer features are used.

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