跳到主要內容

臺灣博碩士論文加值系統

(54.83.119.159) 您好!臺灣時間:2022/01/17 09:38
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:劉欣慈
研究生(外文):Hsin-Tzu Liu
論文名稱:基於Fisherface演算法之人臉辨識系統實現
論文名稱(外文):Implementation of a Face Recognition System Based on the
指導教授:胡竹生胡竹生引用關係
指導教授(外文):Jwu-Sheng Hu
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:中文
論文頁數:66
中文關鍵詞:人臉辨識
外文關鍵詞:face recognitionFisherfaceLDAeigenfacePCA
相關次數:
  • 被引用被引用:9
  • 點閱點閱:3543
  • 評分評分:
  • 下載下載:363
  • 收藏至我的研究室書目清單書目收藏:0
本論文中提出一套有效率的人臉辨識系統,此系統主要分為人臉的偵測與定位以及人臉辨識兩部分.在人臉的偵測與定位方面,主要是建立膚色模型的方法來完成;當找到可能為人臉的候選區域後,接下來的工作就是要確定其是否為可辨識之人臉。首先用侵蝕及擴張的方法偵測人臉特徵的存在,然後利用基於不同角度之線性區別分析的方法去估測人臉的角度並且利用多重觀察的方法在可辨識之人臉區域中選取”純”人臉的部分去做辨識。在辨識的方面主要是採用Fisherface的演算方法並加入投票法則來決定其身份。
在實現此人臉辨識系統時並不需要精確的人臉特徵萃取,並且當應用於一些不同的條件之下如人臉的角度及表情變化,有無戴眼鏡或處於不同光度的環境之下,都可以達到很好的辨識效能。
In this thesis, an efficient face recognition system is proposed. The system mainly consists of two stages: one is face detection and location and another is face recognition. In face detection and location stage, a skin color model is used. The recognizable face is determined when the face candidates are obtained. First, the dilation and erosion functions are used for facial features detection and then, the view-based LDA method is used to determine the face orientation. When obtaining a recognizable face, the “pure” face portion extracted by the method of multiple observations is used for face recognition. In the face recognition stage, the fisherface algorithm is used, and the voting rules are applied to determine the face identification.
In the face recognition system, the precisely facial features extraction is not required, and a satisfactory performance on this system is obtained even if the face image is under some uncontrolled conditions such as face orientation and expression change, wear (i.e. glasses) or not, and different lighting environment.
Content
CHINESE ABSTRACT I
ENGLISH ABSTRACT II
ACKNOWLEDGEMENT III
CONTENT IV
LIST OF TABLES VII
LIST OF FIGURES VIII
CHAPTER 1 INTRODUCTION 1
1.1 MOTIVATION 1
1.2 BACKGROUND 2
1.2.1 Geometric Feature-Based Methods 2
1.2.2 Template-Based Methods 3
1.2.2.1 Karhunen-Loeve Expansion-Based Methods 3
1.2.2.2 Linear Discriminant-Based Methods 4
1.2.3 Model-Based Methods 5
1.2.3.1 Hidden Markov Model-Based Methods 5
1.2.3.2 Active Appearance Model-Based Methods 6
1.2.4 Other Methods 7
1.3 OUTLINE 7
CHAPTER 2 EIGENFACE VS. FISHERFACE 9
2.1 PRINCIPAL COMPONENT ANALYSIS (PCA) 9
2.2 LINEAR DISCRIMINANT ANALYSIS (LDA) 12
2.3 FACE RECOGNITION USING EIGENFACE 14
2.3.1 The Eigenface Algorithm 14
2.3.2 Snapshot Method of Eigenspace Projection 17
2.4 FACE RECOGNITION USING FISHERFACE 18
2.4.1 The Fisherface Algorithm 18
2.5 ANALYSIS AND EXPERIMENT 21
2.5.1 Database 21
2.5.2 Analysis of The Principal Component 23
2.5.3 Testing on Eigenface Algorithm and Fisherface Algorithm 27
Eigenface testing 28
Fisherface testing 29
2.6 SUMMARY 30
CHAPTER 3 SYSTEM ARCHITECTURE 32
3.1 FACE RECOGNITION SYSTEM 32
3.2 FACE DETECTION AND LOCATION 34
3.2.1 A Brief Survey 34
3.2.2 Skin Color Model 37
3.3 RECOGNIZABLE FACE SELECTION 42
3.3.1 Facial Feature Estimation 42
3.3.2 View-Based LDA Method 45
3.3.3 Multiple Observations 46
3.4 FACE RECOGNITION 50
3.4.1 Scale 51
3.4.2 Voting Rules 52
3.5 EXPERIMENTAL RESULT 56
3.6 RE-TRAINING 59
CHAPTER 4 CONCLUSION & FUTURE WORK 61
4.1 CONCLUSION 61
4.2 FUTURE WORK 62
REFERENCE 63
Reference
[1] R. Chellappa, C. Wilson, and S. Sirohey, “Human and machine recognition of faces: a Survey,” in Proceedings of IEEE, vol. 83, May 1995.
[2] L. D. Harmon, M. K, Khan, R. Lasch, and R. F. Raming, “Machine identification of human faces,” Pattern Recognition, pp. 97-110, 1981.
[3] A. J. Goldstein, L. Harmon, and A. Lesk, “Identification of human faces,” in Proceedings of IEEE, pp. 748-760, 1971.
[4] M. Turk and A. Penland, “Face recognition using eigenfaces,” in Proceedings of International Conference on Pattern Recognition, pp. 586-591, 1991.
[5] M. Turk and A. Penland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, vol. 3, pp. 71-86, March 1991.
[6] L. Sirovitch and M. Kirby, “Low-dimensional procedure for the characterization of human faces,” Journal of Optical Society of America, vol. 4, pp. 519-524, March 1987.
[7] A. Penland, B. Moghaddam, and T. Starner. “View-based and modular eigenfaces for face recognition,” in Proceedings of Conference on Computer Vision and Pattern Recognition, pp. 84-91, 1994.
[8] R. Epstein, P. Hallina, and A. Yuille, “5+/- eigenimages suffices: an empirical investigation of low dimensional lighting models,” in Proceedings of the Workshop on Physics-based Modeling in Computer Vision, pp. 108-116, 1995.
[9] K. Etemad and R. Chellapa, “Face recognition using discriminant eigenvectors,” in Proceedings of International Conference on Acoustics, Speech and Signal Processing, pp. 2148-2151, 1996.
[10] P. Belhumeur, J. Hespanha and D. Kriegman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection,” in Proceedings of European Conference on Computer Vision, ECCV’1996, pp. 45-58, 1996.
[11] P. Belhumeur, J. Hespanha and D. Kriegman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, pp. 711-720, July 1997.
[12] K, Fufunaga, Introduction to Statistic Pattern Recognition, Academic Press. 1990.
[13] S. Akamatsu, T. Sasaki, H. Fukumachi, and Y. Suenuga, “A Robust face identification scheme: KL expansion of an invariant feature space,” in SPIE Proceedings::Intelligent Robots and Computer Vision X: Algorithms and Techniques, vol. 1607, pp. 71-81, 1991.
[14] F. Samaria, “Face segmentation for identification using hidden markov models,” in British Machine Vision Conference, 1993.
[15] A. V. Nefian and M. H. Hayes, “Face recognition using an embedded HMM,” in Proceedings of the IEEE Conference on Audio and Video-based Biometric Person Authentication, pp. 19-24, March 1999.
[16] G. J. Edwards, C. J. Taylor, and T. Cootes, “Face recognition using the active appearance model,” in 5th European Conference on Computer Vision, 1998.
[17] A. M. Martinez and A. C. Kak, “PCA versus LDA,” IEEE Trans.Pattern Analysis and Machine Intelligence, Vol. 23, No. 2, Feb. 2001.
[18] S.S. Wilks, Mathematical Statistics, New York: Wiley, 1962.
[19] W. S. Yambor, “Analysis of PCA-based and fisher discrininant-based image recognition algorithm,” Technical Report, Univ. of Colorado State, July 2000.
[20] Y. Moses, Y. Adini, and S. Ullman, “Face recognition: The problem of compensating
for changes in illumination direction,” in European Conference on Computer Vision, pp. 286-296, 1994.
[21] M.-H. Yang, D. Kriegman, and N. Ahuja, “Detecting faces in images: A survey,” IEEE Transaction on Pattern Analysis and Machine Intelligence, 24(1), pp.34-58, 2002.
[22] G. Yang and T. S. Huang, “Human face detection in complex background,” Pattern Recognition, vol. 27, no. 1, pp. 53-63, 1996.
[23] S. A. Sirohey, “Human face segmentation and identification,” Technical Report
CS-TR-3176, Univ. of Maryland, 1993.
[24] H. P. Graf, T. Chen, E. Petajan, and E. Cosatto, “Locating faces and facial parts,” in Proceedings of the First International Workshop Automatic Face and Gesture Recognition, pp. 41-46, 1995.
[25] M. F. Augusteijn and T. L. Skujca, “ Identification of human faces through texure-based feature recognition and Neural Network technology,” in Proceedings of IEEE Conference on Neural Networks, pp. 392-398, 1993.
[26] D. Chai and K. N. Ngan, “Locating facial region of a head-and-shoulders color image,” in Proceedings of the 3rd International Conference on Automatic Face and Gesture Recognition, pp. 124-129, 1998.
[27] S. L. Phung, D. Chai, and A. Bouzerdoum, “A universal and robust human skin color model using neural networks,” in Proceedings of International Joint Conference on Neural Networks, vol. 4, pp. 2844 —2849, 2001.
[28] K. Sobottka and I. Pitas, “Face localization and feature extraction base on shape and color information,” in Proceedings of IEEE International Conference on Image Processing, pp. 483-486, 1996.
[29] E. Osuna, R. Freund, and F. Girosi, “Training support vector machine: An
application to face detection,” in Proceedings of IEEE Conference on Compuer Vision and Pattern Recognition, pp.130-136, 1997.
[30] A. Nefian, “A hidden Markov model-based approach for face detection and recognition,” PhD thesis, Georgia Institute of Technology, Atlanta, GA, August 1999.
[31] M. Propp and A. Samal, “Artificial neural Network architectures for human face detection,” Intelligent Engineering Systems though Artificial Neural Networks, vol. 2, 1992.
[32] L. F. Chen, H. Y. M. Liao, C. C. Han, and J. C. Lin, “Why a statistics-based face recognition system should base its recognition on the pure face portion: A probabilistic decision-based proof,” Proc. 1998, Symposium on Image, Speech, Signal Processing, and Robotics, The Chinese University of Hong Kong, pp. 225-230, September 1998.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top