臺灣博碩士論文加值系統

本論文以人臉辨識為研究目的，研究中以人臉的眼部兩個角點及中心點、嘴部兩個角點及中心點為特徵點，由這六個特徵點彼此間相連而成的線段作為特徵向量，最後再以支持向量機技術結合特徵向量完成人臉影像辨識。
在實驗過程中分別於人臉上、下部分採用膚色分類以及紋理技術並使用直方圖技術找出眼睛與嘴巴之ROI區域，在ROI區域中使用Sobel Edge Detection找出眼睛與嘴巴兩角點(共4個特徵點)，再分別找出兩角點的中心點(共2個特徵點)，此6個特徵點共計算出6個特徵向量作為支持向量機的輸入參數，最後由支持向量機成功完成人臉辨識，經實驗證明本方法能在不同的人臉資料中正確辨識出目標人臉。

In this paper, the human face recognition is used for the purpose of research. The study takes two corners and the center point of eyes in the face, and two corners and the center point of the mouth as the feature points. The six feature points connected to each other are used as the eigenvector, and, finally, the face image recognition is completed by support vector machine technology combined with the eigenvector. In the course of the experiment, the face’s upper and lower parts respectively adapt skin color classification and texture technology, and use the histogram technique to find the ROI area of the eye and mouth. In the ROI area, Sobel Edge Detection is used to find two corners of eyes and two corners of the mouth (a total of four feature points),, and then the center point of the two corners separately (a total of 2 feature points). These six feature points are used to calculate the six eigenvectors as the input parameters of the support vector machine. Finally, the face vector recognition is successfully performed by the support vector machine. It is proved that the method can correctly identify the target face in different face data.

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
第1章、 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 3
1.3 文獻探討 4
1.4 章節概要 8
第2章、 數位影像相關原理 9
2.1 色彩空間轉換 9
2.2 影像二值化 11
2.3 影像濾波器 12
2.4 邊緣檢測 13
2.5 形態學 14
2.6 Support Vector Machine 17
第3章、 演算法 20
3.1 人臉影像偵測 22
3.2 人臉特徵向量ROI自動搜尋 25
3.3 人臉特徵向量設計 33
3.4 特徵向量結合SVM分類 36
第4章、 實驗結果 40
4.1 實驗環境 40
4.2 人臉影像資料庫 41
4.3 人臉影像膚色區域搜尋結果 42
4.4 人臉特徵向量ROI搜尋結果 47
4.5 人臉影像辨識結果 57
第5章、 結論與未來展望 59
5.1 結論 59
5.2 未來展望 59
參考文獻 60

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