本論文於嵌入式系統上發展人臉辨識系統。我們即時擷取臉頰膚
色來做膚色偵測，藉此找到人臉的位置。接著使用IOD 二值化、邊
緣偵測、洪水填滿法和主動式輪廓法等影像處理技術尋找出特徵點位
置，並由特徵點的幾何關係計算出代表人臉的特徵向量。最後，計算
輸入影像和資料庫影像之特徵向量的歐式距離來獲得辨識結果。
在研究中，我們使用分段偵測眼角特徵點來改善特徵點的準確
性，還有加入眼睛輪廓斜率於特徵向量中來增加比對依據，藉此提高
系統的辨識率。

In this thesis, we develop a human face recognition system in
embedded systems. We real-time sample the cheeks skin color to detect
skin color. Then we can find out the coordinates of human face. The
image processing techniques including intensity optical density binary,
edge detection, flood fill and active contour etc, are used to find out the
coordinates of feature points, and then to calculate eigenvectors by
feature point geometry relations. These eigenvectors denote human face.
Finally, we calculate the Euclidean distance between eigenvector of input
image and eigenvector of database image to get the recognizing result.
In this study, we use new method to improve accuracy of canthus
feature points and let slope of eye outline join to eigenvector to increase
the recognition capability.

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