臺灣博碩士論文加值系統

近年來生物資訊蓬勃發展，尤其是生物特徵辨識更是熱門的研究領域，本研究以人臉作為生物特徵進行辨識。將要處理的影像使用Paul Viola和Michael J. Jones所提出的強健即時人臉偵測(Robust Real-Time Face Detection)技術取得人臉位置，接著將所得之人臉進行五官分割，再將分割所得之左眼、右眼、鼻子、嘴巴和整臉使用主值成份分析(PCA, Principal Component Analysis)取得特徵，最後利用支持向量機(SVM, Support Vector Machine)將各別特徵做分類並投票以判斷該人臉。

In recent years, vigorous development of biological information, in particular, biometrics is a hot research field. In this thesis, a new system for human face recognition will be proposed and developed. First the human face of an image is processed and detected by Paul Viola and Michael J. Jones proposed robust real-time face detection technology. The second step is performing the human face segmentation to obtain the eyes, nose and mouth. Third, the principal component analysis (PCA) is applied to obtain the characteristics of resulted eyes, nose, mouth and entire face. Finally, the support vector machine (SVM) is used as a tool for classifying each feature and to have a vote for outcome decision.

目錄
致謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 VII
表目錄 XI
第一章 緒論 1
1-1 研究動機 1
1-2 研究目的 3
1-3 論文架構 4
第二章 數位影像處理 5
2-1 影像灰階化 5
2-2 影像平滑化 6
2-3 影像邊緣偵測 8
2-3-1 Sobel技術介紹 8
2-3-2 Canny技術介紹 10
2-4 影像投影 14
2-4-1 水平投影 15
2-4-2 垂直投影 16
2-5 影像內插法 17
2-6 圓形霍夫轉換(Circular Hough Transform) 22
第三章 人臉偵測 25
3-1 積分影像(Integral Image) 26
3-2 AdaBoost特徵分類器 28
3-3 層疊(Cascade)分類器 31
第四章 特徵抽取與分類技術 32
4-1 主值成份分析 32
4-1-1 主要成份分析之理論 33
4-2 支持向量機 37
4-2-1 VC維概念 38
4-2-2 結構風險最小化 38
4-2-3 最佳分類超平面 39
4-2-4 線性可分離 42
4-2-5 線性不可分離 44
4-2-6 非線性可分離 45
4-2-7 總結SVM 48
4-3 歐氏距離 48
第五章 人臉登錄系統 49
5-1 五官區塊定位之分割技術 49
5-2 實驗環境 55
5-3 系統架構及流程 56
5-4 系統介面 58
5-5 實驗結果 67
第六章 結論與未來展望 70
6-1 結論 70
6-2 未來展望 71
參考文獻 72

參考文獻
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[ 21 ]http://cvc.yale.edu/projects/yalefaces/yalefaces.html (Yale人臉資料庫)
[ 22 ]http://www2.imm.dtu.dk/~aam/ (IMM人臉資料庫)
[ 23 ]http://www.csie.ntu.edu.tw/~cjlin/ (SVM程式參考)