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研究生:倪豐洲
研究生(外文):Feng-Chou Ni
論文名稱:結合PCA與灰色理論之人臉辨識系統
論文名稱(外文):Combining PCA with Gray Theory in Human Face Recognition System
指導教授:陳永平陳永平引用關係
指導教授(外文):Yon-Ping Chen
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電機與控制工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
中文關鍵詞:人臉辨識特徵臉主要元素分析灰色理論
外文關鍵詞:Human Face RecognitionEigenfacePCAGray Theory
相關次數:
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本論文利用結合PCA與灰色理論來建立一套人臉辨識系統. 我們利用加入一些影像前處理,如用兩眼的間距來做正規化,用兩眼中心來做正臉位置的校準及利用統計正臉的平均灰度值來做平均灰度值的補償,來增加PCA理論的辨識率.另一方面,在第二階段辨識的方法是利用幾何特徵值來補償抽象特徵值PCA理論的不足.而幾何特徵值是利用影像處理常用的統計法,色調特性及邊緣偵測等技術將幾何特徵有效率及準確地求得. 利用計算第一階段用PCA理論所得結果的準確率將兩階段式不同辨識方法及特徵值結合.經過實驗過後可以很清楚的得到約93%的辨識率,而所花的時間在Pentium 700M Hz的電腦上平均不到1秒鐘.
This thesis proposes a human face recognition system combining PCA theory with Gray theory. Recognition rate of PCA theory can be improved by several image pre-processing, such as using distance between two eyes for normalization, using center of two eyes for adjustment and using gray balance. On the other hand, recognition of geometric features can be used to compensate recognition of abstract features using PCA. Several general image processings are used in the proposed system, such as histogram, color character and edge detection, to extract geometric features accurately and efficiently. Two recognition methods using distinct features are combined by calculating accuracy index of the result obtained from PCA. The proposed system obviously obtains about 93% recognition rate in several experiments. It spends less than one second in recognition on the Pentium 700M Hz computer.
Contents
Abstract (in Chinese)…………………………………………………………………i
Abstract (in English) ………………………………………………………………...ii
Acknowledgments…………………………………………………………..iii
Contents………………………………………………………………………….iv
List of Tables……………………………………………………………………v
List of Figures…………………………………………………………………vi
1 Introduction……………………………………………………………………..1
1.1 Motivation…………………………………………………………………..1
1.2 Review of related work…………………………………………………..…2
1.3 The proposed system…………………………………………………..……3
1.4 Organization of thesis……………………………………………….………3
2 Relative Image Processing……………………………………………………...4
2.1 The HSI color model………………………………………………………..4
2.2 Conversion from RGB to HSI………………………………………………5
2.3 Histogram processing……………………………………………………….6
2.4 Edge processing..…………………………………………………………...8
2.5 Principal Component Analysis (PCA).……………………………………..9
3 Introduction to the Human Face Recognition System………………………12
3.1 Flow chart of the proposed system………………………………………..13
3.2 Introduction to each work of whole system……………………………….14
4 Pre-process of Human Face Recognition System……………………………17
4.1 Locating the human face…………………………………………………..17
4.2 Locating eyes and eyebrows………………………………………………20
4.3 Locating mouth……………………………………………………………22
4.4 Locating nose……………………………………………………………...23
4.5 Face model………………………………………………………………...24
4.6 Extracting the geometric features of eye…………………………………..26
4.7 Extracting the geometric features of mouth……………………………….31
4.8 Extracting the geometric feature of nose………………………………….36
5 Human Face Recognition System…………………………………………….37
5.1 Database…………………………………………………………………...37
5.2 Recognition using PCA……………………………………………………39
5.3 Recognition using Gray Theory…………………………………………...41
6 Experiment Result…………………………………………………………….42
6.1 Extraction result…………………………………………………………...43
6.2 Result of recognition of geometric features……………………………….43
6.2.1 Experiment 1……………………………………………………44
6.2.2 Experiment 2……………………………………………………44
6.3 Result of recognition of abstract features…………………….……………45
6.3.1 Experiment 3……………………………………………………45
6.3.2 Experiment 4……………………………………………………46
6.4 Effect of different number of eigenface…………………………………...47
6.4.1 Experiment 5……………………………………………………47
6.5 Result of the proposed system……………………………………………..48
7 Conclusion……………………………………………………………………...51
7.1 Conclusion…………………………………………………………………51
7.2 Future work………………………………………………………………..51
Bibliography………………………………………………………………………...52
Bibliography
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