基于PCA的人脸识别研究
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摘要
人脸识别技术，作为目前模式识别领域研究的热点也是难点之一，其最早提出可以追溯到1888年。然而，到目前为止，由于人脸识别问题自身的复杂性，使得虽然有众多科学研究人员潜心研究多年，也做出了许多的成果，但离彻底解决并达到实用，仍旧有很多关键性的问题需要解决。本文结合研究生阶段参与教研室的科研项目，对人脸识别做了一定的研究。论文首先介绍了人脸识别的背景、研究范围以及方法，对人脸识别领域的一些理论方法作了总体的介绍。本文中所采用的人脸识别方法是比较经典的PCA(Principle Component Analysis，主成分分析)。主要工作包括：
(1)、结合FERET人脸库对人脸识别的预处理方法作了较为详细的介绍。预处理的方法包括几何校正、掩模、直方图均衡化、像素灰度值归一化。另外针对人脸库中图像尺寸太大而导致的计算量问题，采用了两种图像缩放的方法：灰度插值和小波分解。
(2)、介绍了PCA人脸识别的方法，在大量实验的基础上对PCA在各种情况下的性能作了详细的分析，得到了一些有意义的结论，掌握了各种不同的参数设置对识别率所造成的影响。
(3)、实现了一个基于PCA的实时人脸检测识别原型系统。
Techniques for face recognition were proposed by Francis Gallon as early as 1888[1]. In recent years considerable progress has been made in the area of face recognition. Through the development of techniques like Eigenfaces computers can now outperform humans in many face recognition tasks, particularly those in which large databases of faces must be searched. Whilst these methods performs extremely well under constrained conditions, the problem of face recognition under gross variations remains largely unsolved. This thesis details the PCA(Principle Component Analysis) algorithm and the development of a real-time face recognition system aimed to operate in constrained environments. Work in this thesis including:
a) Preprocessing of face images on FERET face database[85], the preprocessing method including: geometric normalization, masking, histogram equalization, pixel normalization. Two different scale normalization methods also discussed, i.e. gray image interpolation and wavelet decomposition.
b) Theory of PCA algorithm and its application to face recognition. A great many of experiments have been taken on ORL, Yale, FERET face database, analysis of the results leads to many meaning conclusions on how to improve recognition ration.
c) Implemented a real-time face recognition system based on PCA algorithm.
(1)、结合FERET人脸库对人脸识别的预处理方法作了较为详细的介绍。预处理的方法包括几何校正、掩模、直方图均衡化、像素灰度值归一化。另外针对人脸库中图像尺寸太大而导致的计算量问题，采用了两种图像缩放的方法：灰度插值和小波分解。
(2)、介绍了PCA人脸识别的方法，在大量实验的基础上对PCA在各种情况下的性能作了详细的分析，得到了一些有意义的结论，掌握了各种不同的参数设置对识别率所造成的影响。
(3)、实现了一个基于PCA的实时人脸检测识别原型系统。
Techniques for face recognition were proposed by Francis Gallon as early as 1888[1]. In recent years considerable progress has been made in the area of face recognition. Through the development of techniques like Eigenfaces computers can now outperform humans in many face recognition tasks, particularly those in which large databases of faces must be searched. Whilst these methods performs extremely well under constrained conditions, the problem of face recognition under gross variations remains largely unsolved. This thesis details the PCA(Principle Component Analysis) algorithm and the development of a real-time face recognition system aimed to operate in constrained environments. Work in this thesis including:
a) Preprocessing of face images on FERET face database[85], the preprocessing method including: geometric normalization, masking, histogram equalization, pixel normalization. Two different scale normalization methods also discussed, i.e. gray image interpolation and wavelet decomposition.
b) Theory of PCA algorithm and its application to face recognition. A great many of experiments have been taken on ORL, Yale, FERET face database, analysis of the results leads to many meaning conclusions on how to improve recognition ration.
c) Implemented a real-time face recognition system based on PCA algorithm.
引文
1. F.Galton, "Personal identification and description", Nature, June 21, 1888, 173-177
2. Chellappa R, Wilson C L, Sirohey S. Human and machine recognition of faces: a survey. Proc. IEEE, 1995, 83(5): 705-740.
3．张翠平，苏光大，人脸识别技术综述．中国图象图形学报，2000年，Vol．5．No．11：885-894
4. Baron R. Mechanisms of human facial recognition. Int. J. Man-Machine Studies, 1989,2,283-310.
5. Bruce V. Recognizing faces. London: Erlbaum, 1988.
6. Bichsel M. Perceiving and recognizing faces, Mind and Language, 1990, 342-364.
7. Ellis H et al. Aspects of face processing, Dordrecht: Nijhoff, 1986.
8．金忠．人脸图像特征抽取与维数研究．[博士学位论文]．南京：南京理工大学，1999．
9．周杰．卢春雨，张长水，李衍达，人脸自动识别方法综述．电子学报，2000年第4期
10. Sir Galton F. Numeralized profiles for classification and recognition. Nature 83,127-130(31 March 1910)
11. Allen A L. Personal Descriptions. London, Butterworth,1950.
12. Parker F I, Computer Generated Animation of Faces. In Proceedings ACM ann Conference,1972,1:451-457
13. Goldation R J, Harmon L D, Lesk A B, Man manchine interaction in human face identification. Bell Syst Tech Joumal, 1972, 52:399-427.
14. Kaya Y, Kobayashi K, A Basic study on Human Recognition. In Frontiers of Pattern Recognition. New York: Academic, 1971,265-289.
15. Kanad T, Picture processing system by computer and recognition of human face [Ph D Dissertation].Kyoto: Kynto University, 1973.
16. Samal A and lyengar P A. Automatic recognition and analysis of human faces and facial expressions: a survey. Pattern Recognition, 1992,25(1): 65-77.
17. Turk M and Pentland A. Eigenfaces for recognition. J. Cognitive Neuroscience, 1991, 3(1):71-86.
18. Turk M and Pentland A, Face Recognition using eigenfaces, In Proceeding of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994, pp:586-591.
19. Pentland A et al. View-based modular eigenspaces for face recognition. Prec. IEEE Conf. on CVPR.1994, 84-91.
20. Kirby M and Sirovich L. Application of the KL procedure for the characterization of human faces. IEEE Trans. Pattern Anal. Machine Intell., 1990,12(1): 103-108.
21．彭辉，张长水，荣钢，边肇祺．基于K-L变换的人脸自动识别方法．清华大学学报(自然科学版)，1997，37(3)：67-70
22．黄修武，郭跃飞，杨静宇．基于代数方法的图像特征抽取和识别．南京理工大学学报，1998，22(1)：1-5．
23．郭跃飞，姜志华，杨静宇．一种新的代数特征抽取方法及人脸识别．南京理工大学学报，1997，21(5)：387-390．
24. Juell P, Marsh R. A hierarchical neural network for human face detection. Pattern Recognition, 1996, 29(5):781-787.
25. Schofield A Jet al. A system for counting people in video images using neural networks to
identify the background scene. Pattern Recognition, 1996, 29(8): 1425-1428.
26. Intrator Net al. Face recognition using a hybrid supervised/unsupervised neural network. Pattern Recognition Letters, 1996, 17(1):67-76.
27. Yoon K S et al. Hybrid approaches to frontal view face recognition using the hidden Markov model and neural network. Pattern Recognition, 1998,31(3):283-293
28. Roudey H A. Neural network-based face detection. Proc. of Image Understanding Workshop, 1996:725-735.
29. Ranganath S and Arun K. Face recognition using transform features and neural networks. Pattern Recognition, 1997,30(10): 1615-1622.
30. Rowley H A, Baluja S, and Kanade T. Neural network-based face detection. IEEE Trans. Pattern Anal. Machine Intell., 1998,20(1)
31. Wiskott L et al. Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Machine Intell., 1997, 19(7).
32．高西奇，周洪祥，何振亚．基于小波变换的主元分析人脸图像识别．东南大学学报，1996，26(2)：137-141
33. Adini Y, Moses Y, Ullman S. Face recognition: The problem of compensating for changes in illumination direction. IEEE Trans. Pattern Anal. Machine Intell., 1997, 19(7)
34. Manjunath B S, Chellappa R. A feature based approach to face recognition. Proc. IEEE Computer Soc. Conf. on CVPR, 1992, 373-378.
35. Lain K M and Yan H. An analytic-to-holistic approach for face recognition based on a single frontal view, IEEE Trans. Pattern Anal, Machine Intell., 1998,20(7): 673-687.
36. Ben-Arie J, and Nandy D. A volumetric/iconic frequency domain representation for objects with application for pose invariant face recognition, IEEE Trans. Pattern Anal. Machine Intell., 1998,20(5):449-457.
37. Jia X G and Nixon M S. Extending the feature vector for automatic face recognition. IEEE Trans. Pattern Anal. Machine Intell., 1995,17(12): 1167-1176.
38. Lanitis A, Taylor C J and Cootes T F. Automatic interpretation and coding of face images using flexible models. IEEE Trans. Pattern Anal. Machine Intell., 1997,19(7).
39. Sung K K and Poggio T. Example-based learning for view-based human face detection. IEEE Trans. Pattern Anal. Machine Intell., 1998, 20(1).
40. Darrell T J, Essa I A, and Pentland A P. Task-specific gesture analysis in real-time using interpolated views. IEEE Trans. Pattern Anal. Machine Intell., 1996, 18(12)
41. Takács B. Comparing face images using the modified hausdorff distance. Pattern Recognition, 1998, (31)12:1873-1881.
42. Jeng S H et al. Facial feature detection using geometrical face model: an efficient approach. Pattern Recognition, 1998,(31)3:273-282.
43. Brunelli R and Poggio T. HyperBF networks for gender classification. Proc. DRRPA, Image Understanding Workshop, 1992, 311-314.
44. G.Z.Yang et al. Human face detection in a complex background, Pattern Recognition, 1994,27:53-63.
45. S.H.Jeng et al. Facial feature detection using geometrical face model: an efficient approach, Pattern Recognition, 1998,3:273-282.
46. K.C.Yow et al. Feature-based human face detection, Image and Vision Computing,1997,15:713-735
47. V.Govingdaraju. Int. J. Incidence and lattice calculus, with applications to stochastic geometry and image analysis. Computer Vision,1996,19:129-146.
48. A.Tankus et al. Face Detection by Direct Convexity Estimation, PR Letters,1997,18:913-922.
49. C.H.Lee et al. Automatic human face location in a complex background using motion and color information, Pattern Recognition, 1996,29:1877-1899.
50. Y.Dai and Y.Nakano. Face Texture Model Based on SGLD and Its Application in Face Detectiuon in a Color Scene, Pattern Recognition, Pattem Recognition, 1996,29:1007-1017.
51. E.Saber et al. Frontal-view face detection and facial feature extraction using color, shape, and symmetry-based cost functions, Pattern Recognition Letters, 1998,19:669-680.
52. A.Samal,P.A.Iyengar. Automatic recognition and analysis of human faces and facial expressions: a survey, Pattern Recognition, 1992,25:65-77.
53. R.Chellappa et al. Human and machine recognition of faces: a survey, Procedings of IEEE,1995,83:705-740.
54. H.Zabrodsky et al. Symmetry as a Continuous Feature, IEEE Trans. PAMI,1995,17:1154-1166.
55. D.Reisfeld et al. Int. J. Context-Free Attentional Operators: The Generalized Symmetry Transform, Computer Vision, 1995,14:119-130.
56. Y.Adini et al. Face Recognition: The Problem of Compensating for Changes in Illumination Direction, IEEE Trans. PAMI,1997,19:721-732.
57. X.Iia, MS. Regularization of inverse visual problems involving discontinuities, IEEE Trans PAMI,1995,17:1167-1176.
58. R.Brunelli,T.Piggio. Dyanmic 3D Models with Local and Global Deformations: Deformable Superquadrics, IEEE Trans. PAMI,1993,15:1042-1052.
59. I.J.Cox,J.Ghosn,P.N.Yianilos. Feature-Based Face Recognition Using Mixture-Distance, CVPR's96:209-216
60. N.Roder,X.Li. Robust Image Segmentation Using Genetic Algorithm with a Fuzzy Measure, Pattern Recognition, 1996,29:209-216.
61. A.L.Yuille.J. Handbook of Research on Face Processing, Cognitive Neuroscience,1989,3:59-70.
62. K.M.Lam,H.Yan. Locating and extracting the eye in human face images, Pattern Recognition, 1995,29:771-779.
63. J.Ydeng,F.Lai. Region-based template deformation and masking for eye-feature extraction and description, Pattern Recognition,1997,30:403-419.
64. H.Peng,D. Dual Elgenspace Method for Human Face recognition, Electronics Letters, 1997,33:283-284.
65. K.L.Swets and J.Weng. Using Discriminant Eigenfeatures for Image Retrieval, IEEE Trans. PAMI,1996,18:831-836.
66. P.N.Belhumeur et al. Eigenface vs. Fisherfaces: Recognition Using Class Specific Linear Projection, IEEE Trans. PAMI, 1997,19:711-720.
67. D.Velentin et al. Cormectionist Models of Face Processing-A Survey, Pattern Recognition, 1994,27:1209-1230.
68. P.S.Penev, J.J.Atick. The Invisible hand Algorithm: Solving the assignment Problem with statistical physics, Network Compution&Neural Network,1996,7:477-490.
69. M.Lades et al. Distortion invariant object recognition in dynamic link architecture, IEEE
Trans. Computer, 1993,42:300-311.
70. J.Zhang et al. Proc. Face recognition: eigenface, elastic matching, and neural nets, IEEE,1997,85:1423-1435.
71. C.Nastar et al. matching and recognition using learned deformations, Computer Vision and Image Understanding, 1997,65:179-191.
72. B.Moghaddam et al. Active Face Tracking and Pose Estimation in an Interactive, CVPR,1996
73. A.Lanitis et al. An automatic face identification system using flexible appearance models, Image & Vision Computing, 1995,13:393-401.
74. A.Lanitis et al. Automatic Interpretation and Coding of Face Images Using Flexible Models, IEEE Trans. PAMI,1997,19:743-756.
75. S.Y.Lee et al. Recognition of Human Front Faces Using Knowledge-based feature Extraction and Neuro-Fuzzy Algorithm, Face recognition: a hybrid neural network approach, Pattern Recognition, 1996,29:1863-1876.
76. S.Lawrence et al. Face recognition: a hybrid neural network approach, IEEE Trans. NN,1997,8:98-113.
77. S.Lin,S.Y.Kung,et al. Face recognition/detection by probabilistic decision-based neural network, IEEE Trans. NN,1997,8:114-132.
78. S.Gutta,H.Wechsler. Face Recognition Using Hybrid Classifiers, Pattern Recognition, 1997,30:539-553.
79. P.J.Philips. Matching pursuit filters applied to face identification, IEEE Trans. Image Processing, 1998,7:1150-1164.
80. C.Y.Lu et al. Face Location based on directional symmetry transform, ICNNB,1998,645-648.
81. F.Goudail et al. Face recognition system using local autocorrelations and multiscale integration, IEEE Trans. PAMI, 1996,18:1024-1028.
82. J.Benarie,D.Nandy. Frequency Domain Representation for Objects With Application for Pose Invariant Face Recognition, IEEE Trans. PAMI, 1998,20:449-457.
83. T.Vetter. Int. J. Synthestis of novel views from a single face image, Computer Vision, 1998,28:103-116.
84. A. R. Mirhosseini. Human face image recognition-an evidence aggregation approach, Computer Vision and Image Understanding, 1998,71:213-230.
85. EJ. Phillips, H. Wechsler, J. Huang, and P.Rauss, The Feret database and evaluation procedure for face recognition algorithms, Image and Vision Computing J, Vol.16, No.5, pp 295-306, 1998
86. Ross Beveridge, Evaluation of face recognition algorithm web site: http:\\cs.colostate.edu\evalfacerec
87. ORL web site: http://www.cam-orl.co.uk./
88．任明武，数字图像处理课程讲义，南京理工大学计算机系．2002年
89．陈武凡．小波分析及其在图像处理中的应用．北京：科学出版社，2002．
90．孙兆林，Matlab 6.x图像处理，北京：清华大学出版社，2002．
91．边肇祺、张学工等，模式识别(第二版)，北京：清华大学出版社，2002．3
92. Yambor W., Draper B., and Beveridge J.R., Analysis of PCA-based Face Recognition Algorithms: Eigenvector Selection and Distance Measures. Second Workshop on Empirical Evaluation Methods in Computer Vision, 2000
93．杨健．线性投影分析的理论与算法及其在特征抽取中的应用研究．[博士学位论文]．南
京：南京理工大学，2002．
94．朱志刚，数字图像处理讲义，清华大学计算机系
95. EJonathon Phillips, Patrick Grother, etc, Face Recognition Vendor Test'2002 Overview and Summary, March 2003, http://www.nist.gov/humanid/feret/
96. Jianxin Wu, Zhi-Hua Zhou, Face recognition with one training image per person, Pattern Recognition Letters23(2002) 1711-1719.
97. Microsoft, MSDN, http://msdn.microsoft.com
98．周长发，科学与工程数值算法(Visual C++版)，清华大学出版社，ISBN：7-302-06034-7
2. Chellappa R, Wilson C L, Sirohey S. Human and machine recognition of faces: a survey. Proc. IEEE, 1995, 83(5): 705-740.
3．张翠平，苏光大，人脸识别技术综述．中国图象图形学报，2000年，Vol．5．No．11：885-894
4. Baron R. Mechanisms of human facial recognition. Int. J. Man-Machine Studies, 1989,2,283-310.
5. Bruce V. Recognizing faces. London: Erlbaum, 1988.
6. Bichsel M. Perceiving and recognizing faces, Mind and Language, 1990, 342-364.
7. Ellis H et al. Aspects of face processing, Dordrecht: Nijhoff, 1986.
8．金忠．人脸图像特征抽取与维数研究．[博士学位论文]．南京：南京理工大学，1999．
9．周杰．卢春雨，张长水，李衍达，人脸自动识别方法综述．电子学报，2000年第4期
10. Sir Galton F. Numeralized profiles for classification and recognition. Nature 83,127-130(31 March 1910)
11. Allen A L. Personal Descriptions. London, Butterworth,1950.
12. Parker F I, Computer Generated Animation of Faces. In Proceedings ACM ann Conference,1972,1:451-457
13. Goldation R J, Harmon L D, Lesk A B, Man manchine interaction in human face identification. Bell Syst Tech Joumal, 1972, 52:399-427.
14. Kaya Y, Kobayashi K, A Basic study on Human Recognition. In Frontiers of Pattern Recognition. New York: Academic, 1971,265-289.
15. Kanad T, Picture processing system by computer and recognition of human face [Ph D Dissertation].Kyoto: Kynto University, 1973.
16. Samal A and lyengar P A. Automatic recognition and analysis of human faces and facial expressions: a survey. Pattern Recognition, 1992,25(1): 65-77.
17. Turk M and Pentland A. Eigenfaces for recognition. J. Cognitive Neuroscience, 1991, 3(1):71-86.
18. Turk M and Pentland A, Face Recognition using eigenfaces, In Proceeding of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994, pp:586-591.
19. Pentland A et al. View-based modular eigenspaces for face recognition. Prec. IEEE Conf. on CVPR.1994, 84-91.
20. Kirby M and Sirovich L. Application of the KL procedure for the characterization of human faces. IEEE Trans. Pattern Anal. Machine Intell., 1990,12(1): 103-108.
21．彭辉，张长水，荣钢，边肇祺．基于K-L变换的人脸自动识别方法．清华大学学报(自然科学版)，1997，37(3)：67-70
22．黄修武，郭跃飞，杨静宇．基于代数方法的图像特征抽取和识别．南京理工大学学报，1998，22(1)：1-5．
23．郭跃飞，姜志华，杨静宇．一种新的代数特征抽取方法及人脸识别．南京理工大学学报，1997，21(5)：387-390．
24. Juell P, Marsh R. A hierarchical neural network for human face detection. Pattern Recognition, 1996, 29(5):781-787.
25. Schofield A Jet al. A system for counting people in video images using neural networks to
identify the background scene. Pattern Recognition, 1996, 29(8): 1425-1428.
26. Intrator Net al. Face recognition using a hybrid supervised/unsupervised neural network. Pattern Recognition Letters, 1996, 17(1):67-76.
27. Yoon K S et al. Hybrid approaches to frontal view face recognition using the hidden Markov model and neural network. Pattern Recognition, 1998,31(3):283-293
28. Roudey H A. Neural network-based face detection. Proc. of Image Understanding Workshop, 1996:725-735.
29. Ranganath S and Arun K. Face recognition using transform features and neural networks. Pattern Recognition, 1997,30(10): 1615-1622.
30. Rowley H A, Baluja S, and Kanade T. Neural network-based face detection. IEEE Trans. Pattern Anal. Machine Intell., 1998,20(1)
31. Wiskott L et al. Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Machine Intell., 1997, 19(7).
32．高西奇，周洪祥，何振亚．基于小波变换的主元分析人脸图像识别．东南大学学报，1996，26(2)：137-141
33. Adini Y, Moses Y, Ullman S. Face recognition: The problem of compensating for changes in illumination direction. IEEE Trans. Pattern Anal. Machine Intell., 1997, 19(7)
34. Manjunath B S, Chellappa R. A feature based approach to face recognition. Proc. IEEE Computer Soc. Conf. on CVPR, 1992, 373-378.
35. Lain K M and Yan H. An analytic-to-holistic approach for face recognition based on a single frontal view, IEEE Trans. Pattern Anal, Machine Intell., 1998,20(7): 673-687.
36. Ben-Arie J, and Nandy D. A volumetric/iconic frequency domain representation for objects with application for pose invariant face recognition, IEEE Trans. Pattern Anal. Machine Intell., 1998,20(5):449-457.
37. Jia X G and Nixon M S. Extending the feature vector for automatic face recognition. IEEE Trans. Pattern Anal. Machine Intell., 1995,17(12): 1167-1176.
38. Lanitis A, Taylor C J and Cootes T F. Automatic interpretation and coding of face images using flexible models. IEEE Trans. Pattern Anal. Machine Intell., 1997,19(7).
39. Sung K K and Poggio T. Example-based learning for view-based human face detection. IEEE Trans. Pattern Anal. Machine Intell., 1998, 20(1).
40. Darrell T J, Essa I A, and Pentland A P. Task-specific gesture analysis in real-time using interpolated views. IEEE Trans. Pattern Anal. Machine Intell., 1996, 18(12)
41. Takács B. Comparing face images using the modified hausdorff distance. Pattern Recognition, 1998, (31)12:1873-1881.
42. Jeng S H et al. Facial feature detection using geometrical face model: an efficient approach. Pattern Recognition, 1998,(31)3:273-282.
43. Brunelli R and Poggio T. HyperBF networks for gender classification. Proc. DRRPA, Image Understanding Workshop, 1992, 311-314.
44. G.Z.Yang et al. Human face detection in a complex background, Pattern Recognition, 1994,27:53-63.
45. S.H.Jeng et al. Facial feature detection using geometrical face model: an efficient approach, Pattern Recognition, 1998,3:273-282.
46. K.C.Yow et al. Feature-based human face detection, Image and Vision Computing,1997,15:713-735
47. V.Govingdaraju. Int. J. Incidence and lattice calculus, with applications to stochastic geometry and image analysis. Computer Vision,1996,19:129-146.
48. A.Tankus et al. Face Detection by Direct Convexity Estimation, PR Letters,1997,18:913-922.
49. C.H.Lee et al. Automatic human face location in a complex background using motion and color information, Pattern Recognition, 1996,29:1877-1899.
50. Y.Dai and Y.Nakano. Face Texture Model Based on SGLD and Its Application in Face Detectiuon in a Color Scene, Pattern Recognition, Pattem Recognition, 1996,29:1007-1017.
51. E.Saber et al. Frontal-view face detection and facial feature extraction using color, shape, and symmetry-based cost functions, Pattern Recognition Letters, 1998,19:669-680.
52. A.Samal,P.A.Iyengar. Automatic recognition and analysis of human faces and facial expressions: a survey, Pattern Recognition, 1992,25:65-77.
53. R.Chellappa et al. Human and machine recognition of faces: a survey, Procedings of IEEE,1995,83:705-740.
54. H.Zabrodsky et al. Symmetry as a Continuous Feature, IEEE Trans. PAMI,1995,17:1154-1166.
55. D.Reisfeld et al. Int. J. Context-Free Attentional Operators: The Generalized Symmetry Transform, Computer Vision, 1995,14:119-130.
56. Y.Adini et al. Face Recognition: The Problem of Compensating for Changes in Illumination Direction, IEEE Trans. PAMI,1997,19:721-732.
57. X.Iia, MS. Regularization of inverse visual problems involving discontinuities, IEEE Trans PAMI,1995,17:1167-1176.
58. R.Brunelli,T.Piggio. Dyanmic 3D Models with Local and Global Deformations: Deformable Superquadrics, IEEE Trans. PAMI,1993,15:1042-1052.
59. I.J.Cox,J.Ghosn,P.N.Yianilos. Feature-Based Face Recognition Using Mixture-Distance, CVPR's96:209-216
60. N.Roder,X.Li. Robust Image Segmentation Using Genetic Algorithm with a Fuzzy Measure, Pattern Recognition, 1996,29:209-216.
61. A.L.Yuille.J. Handbook of Research on Face Processing, Cognitive Neuroscience,1989,3:59-70.
62. K.M.Lam,H.Yan. Locating and extracting the eye in human face images, Pattern Recognition, 1995,29:771-779.
63. J.Ydeng,F.Lai. Region-based template deformation and masking for eye-feature extraction and description, Pattern Recognition,1997,30:403-419.
64. H.Peng,D. Dual Elgenspace Method for Human Face recognition, Electronics Letters, 1997,33:283-284.
65. K.L.Swets and J.Weng. Using Discriminant Eigenfeatures for Image Retrieval, IEEE Trans. PAMI,1996,18:831-836.
66. P.N.Belhumeur et al. Eigenface vs. Fisherfaces: Recognition Using Class Specific Linear Projection, IEEE Trans. PAMI, 1997,19:711-720.
67. D.Velentin et al. Cormectionist Models of Face Processing-A Survey, Pattern Recognition, 1994,27:1209-1230.
68. P.S.Penev, J.J.Atick. The Invisible hand Algorithm: Solving the assignment Problem with statistical physics, Network Compution&Neural Network,1996,7:477-490.
69. M.Lades et al. Distortion invariant object recognition in dynamic link architecture, IEEE
Trans. Computer, 1993,42:300-311.
70. J.Zhang et al. Proc. Face recognition: eigenface, elastic matching, and neural nets, IEEE,1997,85:1423-1435.
71. C.Nastar et al. matching and recognition using learned deformations, Computer Vision and Image Understanding, 1997,65:179-191.
72. B.Moghaddam et al. Active Face Tracking and Pose Estimation in an Interactive, CVPR,1996
73. A.Lanitis et al. An automatic face identification system using flexible appearance models, Image & Vision Computing, 1995,13:393-401.
74. A.Lanitis et al. Automatic Interpretation and Coding of Face Images Using Flexible Models, IEEE Trans. PAMI,1997,19:743-756.
75. S.Y.Lee et al. Recognition of Human Front Faces Using Knowledge-based feature Extraction and Neuro-Fuzzy Algorithm, Face recognition: a hybrid neural network approach, Pattern Recognition, 1996,29:1863-1876.
76. S.Lawrence et al. Face recognition: a hybrid neural network approach, IEEE Trans. NN,1997,8:98-113.
77. S.Lin,S.Y.Kung,et al. Face recognition/detection by probabilistic decision-based neural network, IEEE Trans. NN,1997,8:114-132.
78. S.Gutta,H.Wechsler. Face Recognition Using Hybrid Classifiers, Pattern Recognition, 1997,30:539-553.
79. P.J.Philips. Matching pursuit filters applied to face identification, IEEE Trans. Image Processing, 1998,7:1150-1164.
80. C.Y.Lu et al. Face Location based on directional symmetry transform, ICNNB,1998,645-648.
81. F.Goudail et al. Face recognition system using local autocorrelations and multiscale integration, IEEE Trans. PAMI, 1996,18:1024-1028.
82. J.Benarie,D.Nandy. Frequency Domain Representation for Objects With Application for Pose Invariant Face Recognition, IEEE Trans. PAMI, 1998,20:449-457.
83. T.Vetter. Int. J. Synthestis of novel views from a single face image, Computer Vision, 1998,28:103-116.
84. A. R. Mirhosseini. Human face image recognition-an evidence aggregation approach, Computer Vision and Image Understanding, 1998,71:213-230.
85. EJ. Phillips, H. Wechsler, J. Huang, and P.Rauss, The Feret database and evaluation procedure for face recognition algorithms, Image and Vision Computing J, Vol.16, No.5, pp 295-306, 1998
86. Ross Beveridge, Evaluation of face recognition algorithm web site: http:\\cs.colostate.edu\evalfacerec
87. ORL web site: http://www.cam-orl.co.uk./
88．任明武，数字图像处理课程讲义，南京理工大学计算机系．2002年
89．陈武凡．小波分析及其在图像处理中的应用．北京：科学出版社，2002．
90．孙兆林，Matlab 6.x图像处理，北京：清华大学出版社，2002．
91．边肇祺、张学工等，模式识别(第二版)，北京：清华大学出版社，2002．3
92. Yambor W., Draper B., and Beveridge J.R., Analysis of PCA-based Face Recognition Algorithms: Eigenvector Selection and Distance Measures. Second Workshop on Empirical Evaluation Methods in Computer Vision, 2000
93．杨健．线性投影分析的理论与算法及其在特征抽取中的应用研究．[博士学位论文]．南
京：南京理工大学，2002．
94．朱志刚，数字图像处理讲义，清华大学计算机系
95. EJonathon Phillips, Patrick Grother, etc, Face Recognition Vendor Test'2002 Overview and Summary, March 2003, http://www.nist.gov/humanid/feret/
96. Jianxin Wu, Zhi-Hua Zhou, Face recognition with one training image per person, Pattern Recognition Letters23(2002) 1711-1719.
97. Microsoft, MSDN, http://msdn.microsoft.com
98．周长发，科学与工程数值算法(Visual C++版)，清华大学出版社，ISBN：7-302-06034-7
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