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研究生:陳豪宇
研究生(外文):Hoa-Yu Chan
論文名稱:以元件為基礎之人臉辨識
論文名稱(外文):Component-based Face Recognition
指導教授:傅心家傅心家引用關係
指導教授(外文):Hsin-Chia Fu
學位類別:碩士
校院名稱:國立交通大學
系所名稱:資訊工程系
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:中文
論文頁數:46
中文關鍵詞:人臉辨識
外文關鍵詞:face recognitioncomponent-basedface detector
相關次數:
  • 被引用被引用:3
  • 點閱點閱:285
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本論文的目的是利用人臉對稱與鼻尖凸出於雙眼與嘴的假設,算出偏轉人像之概略的頭部旋轉資訊,並將這一資訊使用在修正不同元件component)
在角度改變時所應修正的混合比例上,來提升辨識正確率。以往利用元件間相對位置的資訊時,會想到算出每個人特有的臉部關係做為辨識內容,而忽略了算出臉部角度的資訊也可以提供辨識時混合元件的資訊。本辨識系統基於不同元件在不同人臉角度下應該使用不同重要性的混合比例,利用以元件為基礎之人臉偵測,獲得雙眼、鼻、嘴等四個元件的位置與四個由灰階值構成的特徵向量。由四個元件位置可算出概略的頭部旋轉資訊。再利用一套兩層式處理的辨識方法,第一層是各自元件利用Support Vector Machine (SVM)訓練出辨識某人相似程度的判斷器,第二層則是利用SVM訓練混合頭部旋轉資訊與某人相似程度函數中的系數。根據實驗結果,在精確偵測元件位置與訓練資料較少的情況下,兩層架構比單層架構得到較好的辨識率。
This paper use hypothesis of that face symmetrical and nose which stick out the eyes and mouth to calculate the information of the biased of the head turn, and use this information to modify the weight of different component. When Using the information of the relation between the components, we would ignore the information that the face turn angle can be used to provide the information. When we use the component based face detection, we can obtain the position of the eyes , nose,and mouth and the four feature vectors which are compose of four gray value. According to the position of four component, we can calculate the information of how angle which head turns. After this, we use the two layers recognition method. The layer one, we use each of components and the support vector machine (SVM) to train the recognized machine which can recognize some level of similarity. The layer two, we use the SVM to train the coefficient of the similar degree function and the
information of the mixed turn of the head. According the result of experiment in the precisely detect the position of component and less training information state, two layer architecture has better recognition rate than single layer architecture.
{第一章}導論---------------1
{1.1}前言---------------1
{1.2}相關研究---------------2
{1.3}研究目標---------------3
{1.4}章節介紹---------------4
{第二章}文獻探討---------------5
{2.1}Support Vector Machine---------------5
{2.1.1}對於可完全分離的資料---------------6
{2.1.2}對於不可完全分離的資料---------------7
{2.1.3}高維度的分割曲面---------------9
{2.1.4}多類別問題---------------9
{2.2}以元件為基礎之人臉辨識相關研究---------------10
{第三章}人臉辨識器的設計---------------13
{3.1}以元件為基礎的人臉偵測器---------------13
{3.2}頭部左右旋轉資訊的取得---------------14
{3.3}系統的建立---------------15
{3.3.1}原始系統---------------15
{3.3.2}雙層架構系統---------------16
{3.3.3}加上$R$的雙層架構系統---------------18
{第四章}實作---------------24
{4.1}人臉偵測器---------------24
{4.2}人臉辨識器---------------25
{第五章}實驗---------------27
{5.1}實驗設計---------------27
{5.2}測試用的人臉資料庫---------------27
{5.3}系統的測試---------------28
{5.4}實驗結果與分析---------------29
{5.4.1}AT&T Data Base---------------29
{5.4.2}UMIST Data Base---------------30
{5.4.3}UMIST Data Base 手動資料---------------31
{第六章}結論及未來工作---------------33
{6.1}結論---------------33
{6.2}未來工作---------------33
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[2] R. Chellappa, C.L. Wilson, S. Sirohey, "Human and machine recognition of Faces: A survey," Proceedings of the IEEE, Volume: 83 Issue: 5, May 1995 Page(s): 705-741.
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[4] Bernd Heisele, Tomaso Poggio, Massimiliano Pontil, "Face Detection in Still Gray Image," A.I. Memo No. 1687 C.B.C.L Paper No.187 May,2000.
[5] Bernd Heisele, Purdy Ho, Tomaso Poggio, "Face Recognition with Support Vector Machines: Global versus Component-based Approach," Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, Volume: 2,2001.
[6] A.Z. Kouzani, F. He, K. Sammut, "Multiresolution eigenface-components,"TENCON ''97. IEEE Region 10 Annual Conference.Speech and Image Technologies for Computing and Telecommunications, Proceedings of IEEE, Volume: 1, 1997 Page(s): 353 -356 vol.1.
[7] M. Turk, A.P. Pentland, "Eigenfaces for Recognition,"CogNeuro(3), No. 1, 1991, pp.71-96.
[8] B. Moghaddam, A.P. Pentland, "Face Recognition Using View-Baed and Modular Eigenspaces,"Vismod - 301, 1994.
[9] Y. Adini, Y. Moses, S. Ullman, "Face Recognition: The Problem of Compensating for Changes in Illumination Direction," PAMI (19), No.7, July 1997, pp.721-732.
[10] Christopher M. Bishop , Neural Networks for Pattern Recognition.Clarendon press. Oxford 1995.
[11] CHRISTOPHER J.C. BURGES, "A Tutorial on Support Vector Machines for Pattern Recognition," Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
[12] E. Saber and A.M. Tekalp, "Frontal-View Face Detection and Facial Feature Extraction Using Color, Shape and Symmetry Based Cost Functions," Pattern Recognition Letters, vol. 17, no. 8, pp. 669- 680, 1998.
[13] A. Yuille, P. Hallinan, and D. Cohen, "Feature Extraction from Faces Using Deformable Templates,"Int''l J. Computer Vision, vol. 8, no. 2, pp. 99-111, 1992.
[14] M. Venkatraman and V. Govindaraju, "Zero Crossings of a Non-Orthogonal Wavelet Transform for Object Location,"Proc. IEEE Int’l Conf. Image Processing, vol. 3, pp. 57-60, 1995.
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