基于核投影分析的特征抽取及应用研究
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摘要
特征抽取是模式识别研究的最基本的问题之一。无论是人脸识别还是字符识别，抽取有效的鉴别特征是解决问题的关键。核投影分析，包括核主分量分析(KPCA)和核Fisher鉴别分析(KFDA)，是最近刚刚提出的非常有效的非线性特征抽取方法。该文一方面对核投影分析的内涵从理论上进行了补充，另一方面对核投影分析的有关算法进行了较为深入的研究，所提出的算法在人脸识别和字符识别方面得到了较成功的应用。
Foley-Sammon线性鉴别分析(FSDA)是抽取线性特征的有效方法之一。在此基础上，该文借鉴核Fisher鉴别分析的实现思想，提出了一种核Foley-Sammon鉴别分析(核F-S鉴别分析，KFSDA)方法，首先建立KFSDA的两个等价模型，并分析这两个等价模型的解之间的关系，然后从理论上给出KFSDA模型的具体求解方法。分析表明，核Foley-Sammon鉴别分析保留了FSDA能明显降低样本特征之间冗余信息的优点，更重要的是该方法能够有效地抽取样本的非线性特征；另外，KFSDA是对FSDA的进一步拓展。在Concordia University CENPARMI手写体阿拉伯数字数据库上的实验结果验证了所提出方法的有效性。
该文利用核技术把广义最佳鉴别矢量集进行非线性推广，提出一个全新的概念—广义最佳核鉴别矢量集，建立广义最佳核鉴别矢量集的求解模型，从理论上给出广义最佳核鉴别矢量集的具体求解方法。分析表明：用广义最佳核鉴别矢量集所抽取的特征不仅在整体上具有最佳的可分性，而且具有非线性特性；另外，广义最佳核鉴别矢量集是对广义最佳鉴别矢量集的进一步拓展。将广义最佳核鉴别矢量集用于ORL人脸库的识别，识别错误数明显低于已有的方法。
核Fisher鉴别分析(KFDA)已成为抽取非线性特征的最有效方法之一。但是，无论训练样本的数目多少、维数高低，总面临一个奇异性问题，对此在现有的KFDA算法中还没有得到很好的解决。在该文中我们提出了一种最优的核Fisher鉴别分析(OKFDA)方法，从理论上巧妙地解决了奇异情况下最优核鉴别矢量集的求解问题。OKFDA基本思路为把最优核鉴别矢量分为两类，首先优先在核类内散布矩阵的零空间内选择使核类间散布量最大的一组标准正交的特征矢量，即为第一类最优核鉴别矢量，然后在核类内散布矩阵的非零空间内选择使核Fisher鉴别准则达到最大的一组标准的特征矢量，即为第二类最优核鉴别矢量，这样我们就得到了最优核鉴别矢量集，从而相应地抽取出原始样本的非线性最优鉴别特征(共两类)。在FERET人脸库的子库上的实验结果验证了OKFDA方法的有效性。
独立分量分析以其独特的性质在人脸识别中发挥着重要的作用。但是我们知
摘要
博十论文
道即便使用快速的ICA算法(FasilCA)来抽取人脸图象特征都存在着运算量大、
耗时多等问题，为此该文提出了一种新的人脸自动识别方法，首先采用核主分量
分析(KPCA)对原始的人脸图象进行降维，这样不仅突出了人脸图象的主分量
特征，而且考虑了包含图象象素间的非线性关系的高阶统计信息。然后利用
FastICA算法进一步抽取出更加有利于分类的面部特征的主要独立成分，以用来
后面的识别分类。在FERET人脸库的子库上实验结果表明，所提出的方法与基
于FastICA的方法相比识别性能略有提高，更为特出的是在识别速度上显示出很
大的优势。
关键词:模式识别，特征抽取，核技术，核主分量分析，核Fishe:鉴别分析，核
Foley一Sammon鉴别分析，广义最佳核鉴别矢量集，独立分量分析，FasilCA
Feature extraction is one of the elementary problems in the area of pattern recognition. It is the key to the classifier problems such as face identification and handwritten character recognition. The kernel projection analysis, including the kernel principal component analysis (KPCA) and kernel Fisher discriminant analysis (KFDA), is an efficient nonlinear feature extraction method proposed by Scholkopf and Mike et al recently. This paper not only extends the kernel projection analysis theoretically, but also analyzes the associated algorithms on kernel projection analysis. The proposed algorithms can be successfully applied on the face recognition and handwritten character recognition.Foley-Sammon linear discriminant analysis (FSDA) is an efficient linear feature extraction method. Based on FSDA and kernel Fisher discriminant analysis, a kernel Foley-Sammon discriminant analysis (KFSDA) is proposed. Firstly two equivalent models of KFSDA are built and then the relationship between them is analyzed. Lastly the detailed implementation and correspording provement of KFSDA models are given. It can be got that KFSDA can preserve the advantage of FSDA, that the redundant information among sample features can be reduced very well. Moreover KFSDA can effectively extract the sample nonlinear features. Obviously KFSDA is the further extension to FSDA. The experiments based on Concordia University CENPARMI-a database of handwritten Arabic numerals, prove the effectiveness of KFSDA.Based on the kernel methods, this paper extends nonlinearly the generalized optimal set of discriminant vector and proposes a new concept of generalized optimal set of kernel discriminant vector (GOSKDV). The corresponding model of this concept is built and the detailed implemention is given. The analysis shows that the features that are extracted based on GOSKDV have the maximum separability on the whole and nonlinear characteristics. Obviously the GOSKDV is the further extension to the generalized optimal set of discriminant vector. The experimental results based on the ORL face database show that the proposed method is valid.Although the kernel Fisher discriminant analysis (KFDA) has already become one of the most efficient nonlinear feature extraction methods, it always faces the singularity problem. So far the existing algorithms of KFDA have not solved this problem very well. This paper proposed an optimal kernel Fisher discriminant
analysis (OKFDA), which solving the computation of the optimal kernel discriminant vectors in the singular cases. It divides the optimal kernel discriminant vectors into two kinds. First, in the null space of kernel within-class scatter matrix, the normal orthogonal vector group which maximizes the kernel between-class scatter is selected, so the first class of optimal kernel discriminant vectors is got. Then in non-null space of kernel within-class scatter matrix, the normal vector group that maximizes the kernel discriminant criterion is selected, which is the second class of optimal kernel discriminant vectors. Therefore the optimal kernel discriminant vector set is got, which extracts the optimal nonlinear discriminant features (two classes altogether) of original samples. The experimental results based on the sub-set of FERET face database show the effectiveness of OKFDA.Although the independent component analysis (ICA) plays an important role in the field of face recognition due to its good properties, as we all know, the feature extraction of face image even based on the fast ICA algorithm (FastICA) has disadvantages, such as huge-computation and time-consuming. So a new automatic face recognition method is proposed in this paper. Firstly the kernel principal component analysis (KPCA) is used to reduce the dimension of original face image, thus the principal component feature of face image is given prominence and the high order statistical information about the nonlinear relationships among the pixels of face image is considered. Finally, the algorithm of FastICA is used to extract the principal indepe
Foley-Sammon线性鉴别分析(FSDA)是抽取线性特征的有效方法之一。在此基础上，该文借鉴核Fisher鉴别分析的实现思想，提出了一种核Foley-Sammon鉴别分析(核F-S鉴别分析，KFSDA)方法，首先建立KFSDA的两个等价模型，并分析这两个等价模型的解之间的关系，然后从理论上给出KFSDA模型的具体求解方法。分析表明，核Foley-Sammon鉴别分析保留了FSDA能明显降低样本特征之间冗余信息的优点，更重要的是该方法能够有效地抽取样本的非线性特征；另外，KFSDA是对FSDA的进一步拓展。在Concordia University CENPARMI手写体阿拉伯数字数据库上的实验结果验证了所提出方法的有效性。
该文利用核技术把广义最佳鉴别矢量集进行非线性推广，提出一个全新的概念—广义最佳核鉴别矢量集，建立广义最佳核鉴别矢量集的求解模型，从理论上给出广义最佳核鉴别矢量集的具体求解方法。分析表明：用广义最佳核鉴别矢量集所抽取的特征不仅在整体上具有最佳的可分性，而且具有非线性特性；另外，广义最佳核鉴别矢量集是对广义最佳鉴别矢量集的进一步拓展。将广义最佳核鉴别矢量集用于ORL人脸库的识别，识别错误数明显低于已有的方法。
核Fisher鉴别分析(KFDA)已成为抽取非线性特征的最有效方法之一。但是，无论训练样本的数目多少、维数高低，总面临一个奇异性问题，对此在现有的KFDA算法中还没有得到很好的解决。在该文中我们提出了一种最优的核Fisher鉴别分析(OKFDA)方法，从理论上巧妙地解决了奇异情况下最优核鉴别矢量集的求解问题。OKFDA基本思路为把最优核鉴别矢量分为两类，首先优先在核类内散布矩阵的零空间内选择使核类间散布量最大的一组标准正交的特征矢量，即为第一类最优核鉴别矢量，然后在核类内散布矩阵的非零空间内选择使核Fisher鉴别准则达到最大的一组标准的特征矢量，即为第二类最优核鉴别矢量，这样我们就得到了最优核鉴别矢量集，从而相应地抽取出原始样本的非线性最优鉴别特征(共两类)。在FERET人脸库的子库上的实验结果验证了OKFDA方法的有效性。
独立分量分析以其独特的性质在人脸识别中发挥着重要的作用。但是我们知
摘要
博十论文
道即便使用快速的ICA算法(FasilCA)来抽取人脸图象特征都存在着运算量大、
耗时多等问题，为此该文提出了一种新的人脸自动识别方法，首先采用核主分量
分析(KPCA)对原始的人脸图象进行降维，这样不仅突出了人脸图象的主分量
特征，而且考虑了包含图象象素间的非线性关系的高阶统计信息。然后利用
FastICA算法进一步抽取出更加有利于分类的面部特征的主要独立成分，以用来
后面的识别分类。在FERET人脸库的子库上实验结果表明，所提出的方法与基
于FastICA的方法相比识别性能略有提高，更为特出的是在识别速度上显示出很
大的优势。
关键词:模式识别，特征抽取，核技术，核主分量分析，核Fishe:鉴别分析，核
Foley一Sammon鉴别分析，广义最佳核鉴别矢量集，独立分量分析，FasilCA
Feature extraction is one of the elementary problems in the area of pattern recognition. It is the key to the classifier problems such as face identification and handwritten character recognition. The kernel projection analysis, including the kernel principal component analysis (KPCA) and kernel Fisher discriminant analysis (KFDA), is an efficient nonlinear feature extraction method proposed by Scholkopf and Mike et al recently. This paper not only extends the kernel projection analysis theoretically, but also analyzes the associated algorithms on kernel projection analysis. The proposed algorithms can be successfully applied on the face recognition and handwritten character recognition.Foley-Sammon linear discriminant analysis (FSDA) is an efficient linear feature extraction method. Based on FSDA and kernel Fisher discriminant analysis, a kernel Foley-Sammon discriminant analysis (KFSDA) is proposed. Firstly two equivalent models of KFSDA are built and then the relationship between them is analyzed. Lastly the detailed implementation and correspording provement of KFSDA models are given. It can be got that KFSDA can preserve the advantage of FSDA, that the redundant information among sample features can be reduced very well. Moreover KFSDA can effectively extract the sample nonlinear features. Obviously KFSDA is the further extension to FSDA. The experiments based on Concordia University CENPARMI-a database of handwritten Arabic numerals, prove the effectiveness of KFSDA.Based on the kernel methods, this paper extends nonlinearly the generalized optimal set of discriminant vector and proposes a new concept of generalized optimal set of kernel discriminant vector (GOSKDV). The corresponding model of this concept is built and the detailed implemention is given. The analysis shows that the features that are extracted based on GOSKDV have the maximum separability on the whole and nonlinear characteristics. Obviously the GOSKDV is the further extension to the generalized optimal set of discriminant vector. The experimental results based on the ORL face database show that the proposed method is valid.Although the kernel Fisher discriminant analysis (KFDA) has already become one of the most efficient nonlinear feature extraction methods, it always faces the singularity problem. So far the existing algorithms of KFDA have not solved this problem very well. This paper proposed an optimal kernel Fisher discriminant
analysis (OKFDA), which solving the computation of the optimal kernel discriminant vectors in the singular cases. It divides the optimal kernel discriminant vectors into two kinds. First, in the null space of kernel within-class scatter matrix, the normal orthogonal vector group which maximizes the kernel between-class scatter is selected, so the first class of optimal kernel discriminant vectors is got. Then in non-null space of kernel within-class scatter matrix, the normal vector group that maximizes the kernel discriminant criterion is selected, which is the second class of optimal kernel discriminant vectors. Therefore the optimal kernel discriminant vector set is got, which extracts the optimal nonlinear discriminant features (two classes altogether) of original samples. The experimental results based on the sub-set of FERET face database show the effectiveness of OKFDA.Although the independent component analysis (ICA) plays an important role in the field of face recognition due to its good properties, as we all know, the feature extraction of face image even based on the fast ICA algorithm (FastICA) has disadvantages, such as huge-computation and time-consuming. So a new automatic face recognition method is proposed in this paper. Firstly the kernel principal component analysis (KPCA) is used to reduce the dimension of original face image, thus the principal component feature of face image is given prominence and the high order statistical information about the nonlinear relationships among the pixels of face image is considered. Finally, the algorithm of FastICA is used to extract the principal indepe
引文
1. K. Fukunaga. Introduction to Statistical Pattern Recognition. New York: Academic Press, Inc. 2nd ed, 1990
2．边肇祺，张学工．模式识别(第二版)．北京：清华大学出版社，1999
3. Turk M, Pentland A. Eigenfaces for recognition. J. Cognitive Neuroscience, 1991, 3(1): 71-86
4. Liu K, Yang J.-Y. et al, An efficient algorithm for Foley-Sammon optimal set of discriminant vectors by algebraic method. International Journal of Pattern Recognition and Artificial Intelligence, 1992, 6(5): 817-829
5. Y. F. Guo, T. T, Shu, J. Y. Yang, et al.. Feature extraction method based on the generalized Fisher Discriminant criterion and face recognition. Pattern Analysis & Application, 2001, 4(1): 61-66
6. Peter N. Belhumeur, et al. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans.Pattern Anal. Machine Intell, 1997, 19(7): 711-720
7. B. Schlkopf, S. Mika, C.J.C. Burges, P. Knirsch, K. R. Müller, G. Ritsch, A.J. Smola. Input space vs. feature space in kernel-based methods. IEEE Transactions on Neural Networks, 1999, 10(5): 1000-1017
8. K.-R. Müller, S. Mika, G.Rtsch, K. Tsuda, and B. Schlkopf. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 2001, 12(2): 181-201
9. Colin Campbell. Kernel methods: a survey of current techniques. Neurcomputing, 2002, 48: 63-84
10. Alberto Ruiz, Perdro E. Ldpez-de-Teruel. Nonlinear Kernel-Based Statistical Pattern Analysis. IEEE Transactions on Neural Networks, 2001, 12(1): 1045-9227
11. Vladimir N. Vapnik. The Nature of Statistical Learning Theory. Springer, 1995
12．张学工．关于统计学习理论与支持向量机．自动化学报，2000，26(1)：32-42
13. Cortes C, Vapnik V. Support Vector Networks. Machine Learning, 1995, 20: 273-297
14. Cherkassky V, Mulier F. Learning from Data: Concepts, Theory and Methods. NY: John Viley & Sons, 1997
15. Mika, G. Rtsch, J Weston, B. Schlkopf, A. Smola, and K. R. Müller. Constructing descriptive and discriminative non-linear features: Rayleigh coefficients in kernel feature spaces. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2003, 25(5): 623-628
16. S. Mika, G. Ratsch, J. Weston, B. Scholkopf, K. Muller. Fisher discriminant analysis with kernels. In IEEE Neural Networks for Signal Processing Workshop, 1999, 41-48
17. G. Baudat, F. Anouar. Generalized discriminant analysis using a kernel approach. Neural Computation, 2000, 12: 2385-2404
18. B. Scholkopf, A. Smola, and K.R. Muller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 1998, 10(5): 1299-1319
19. B. Scholkopf, A. Smola, K.R. Muller. Kernel principal component analysis. In W. Gerstner, Artificial Neural Networks - ICANN'97, Berlin, 1997, 583-588
20. Volker Roth, Volker Steinhage. Nonlinear discriminant analysis using kernel functions. In S.A.Solla,T.K.Leen,K.-R.MUller, editors. Advance in Neural Information Processing Systems 12, Cambridge, MA: MIT Press, 2000, 568-574
21. K. I. Kim, K. Jung, H. J. Kim. Face recognition using kernel principal component analysis. IEEE Signal Processing Letters (SPL), 2002, 9(2): 40-42
22. K. I. Kim, S. H. Park, H. J. Kim. Kernel principal component analysis for texture classification. IEEE SPL, 2001, 8(2): 39-41
23. K. I. Kim, K. Jung, S. H. Park, H. J. Kim. Texture classification with kernel principal component analysis. IEE Electronics Letters (EL), 2000, 36(12): 1021-1022
24. M. H. Yang. Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods. Proceedings of the Fifth International Conference on Automatic Face and Gesture Recognition (FG 2002), Washington D. C, May, 2002, 215-220
25. M. H. Yang, N. Ahuja, D. Kriegman. Face recognition using kernel Eigenfaces. In Proceedings of the 2000 IEEE International Conference on Image Processing (ICIP 2000), Vancouver, Canada, September, 2000, 1: 37-40
26. Juwei Lu, K.N. Plataniotis, A.N. Venetsanopoulos. Face Recognition Using Kernel Direct Discriminant Analysis Algorithms. IEEE Transactions on Neural Networks, 2003, 14(1): 117-126
27. C. Liu. Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Trans. Pattern Analysis and Machine Intelligence, 2004, 26(5): 572-581
28. Kirby M, Sirovich L. Application of the KL procedure for the characterization of human faces. IEEE Trans. Pattern Anal. Machine Intell., 1990, 12(1): 103-108
29. A. Pentland, B. Moghaddam, T. Starner. View-based and mododular eigenspaces for face recognition. Proc. IEEE Conf. On Computer Vision and Pattern Recognition, 1994, 84-91
30. Turk M and Pentland A., Face recognition using Eigenfaces, Proc. IEEE Conf. On Computer Vision and Pattern Recognition, 1991, 586-591
31. Bischel M, Pentland A. Human face recognition and face image set's topology. CVGIP: Image Understanding, 1994, 59(2): 54-261.
32. Turk M, Pentland A. Face processing: Models for recognition. Proc. Intelligent Robots and Computer Vision Ⅷ, SPIE, 1989, 1, 192: 22-32
33. R. A. Fisher. The use of multiple measurements in taxonomic problems, Annals of Eugenics 7 (1936) 178-188
34. S. S. Wilks. Mathematical Statistics. Wiley, New York, 1962, 577-578
35. R. Duda, P. Hart. Pattern Classification and Scene Analysis. Wiley, New York, 1973
36. J.W. Sammon. An Optimal discriminant plane. IEEE Trans. Computer, 1970, C-19: 826-829
37. Foley D H, Sammon J W Jr.. An optimal set ofdiscriminant vectors. IEEE Trans. Computer. 1975, 24(3): 281-289
38. Duchene J, Leclercq S. An optimal Transformation for discriminant and principal component analysis. IEEE Trans. Pattern Anal. Machine Intell, 1988, 10(6): 978-983
39. Zhong Jin, J. Y. Yang, Z.S. Hu, Z. Lou. Face Recognition based on uncorrelated discriminant transformation. Pattern Recognition, 2001, 34(7): 1405-1416
40．金忠，杨静宇，陆建峰．一种具有统计不相关性的最优鉴别矢量集．计算机学报，1999，22(10)：1105-1108
41. Z. Jin, J.Y. Yang, Z.M. Tang, Z.S. Hu. A theorem on uncorrelated optimal discriminant vectors. Pattern Recognition, 2001, 34(10): 2041-2047
42．金忠，胡钟山，杨静宇，刘克，孙靖夷．手写体数字有效鉴别特征的抽取和识别．计算机研究与发展，1999，36(12)：1484-1489
43．金忠．人脸图像特征抽取与维数研究．南京：南京理工大学博士论文，1999
44．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．南京：南京理工大学博士论文，2002
45．杨健，杨静宇，金忠．最优鉴别特征的抽取及图像识别．计算机研究与发展，2001，38(11)：1331-1336
46. Daniel L. Swets, John Weng. Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Anal. Machine Intell., 1996, 18(8): 831-836
47. Cheng Jun Liu, Harry Wechsler. Robust coding schemes for indexing and retrieval from large face databases. IEEE Trans. Image Processing, 2000, 9(1): 132-137
48. J. Yang, J-y Yang, A.F. Frangi, D. Zhang. Uncorrelated Projection Discriminant Analysis and Its Application to Face Image Feature Extraction. Int. Journal of Pattern Recognition and Artificial Intelligence, 2003, 17(8): 1325-48
49. J. Yang, J-y Yang, A.F. Frangi. Combined Fisherfaces framework, Image and Vision Computing, 2003, 21 (12): 1037-44
50
50．杨健，杨静宇，叶晖．Fisher线性鉴别分析的理论及其应用．自动化学报，2003，29(4)：481-494
51. Jian Yang, Jing-yu Yang. Why can LDA be performed in PCA transformed space? Pattern Recognition, 2003, 36(2): 563-566
52. Jian Yang, Jing-yu Yang. Optimal FLD algorithm for facial feature extraction. SPIE Proc. Intelligent Robots and Computer Vision ⅩⅩ: Algorithms, techniques, and Active Vision, October, 2001, 4572: 438-444
53. Hong Z. Q., Yang J.Y. et al.. Optimal discriminant plane for a small number of samples and design method of classifier on the plane. Pattern Recognition, 1991, 24(4): 317-324
54. Li-Fen Chert, H.-Y. Mark Liao, Ja-Chen Lin, et al.. Why recognition in statistics-based face recognition system should based on pure face portion: a probabilistic decision-based proof. Pattern Recognition, 2001, 34: 317-324
55. Li-Fen Chert, H.-Y. Mark Liao, M.-T. Ko, et al.. A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognition, 2000, 33(10): 1713-1726
56．郭跃飞．人脸图像的代数特征抽取与最佳鉴别矢量的研究．南京：南京理工大学博士论文，2000
57．郭跃飞，黄修武，杨静宇等．一种求解Fisher最佳鉴别矢量的新方法及人脸识别．中国图象图形学报，1999，4(A)(2)：95-98
58. Hua Yu, Jie Yang. A direct LDA algorithm for high-dimensional data—with application to face recognition. Pattern Recognition, 2001, 34(11): 2067-2070
59. Vapnik V, Levin E, Le Cun Y. Measuring the VC dimension of a learning machine. Neural Computation, 1994, 6: 851-876
60. Osuna E, Freund R, Girosi F. An Inproved Training Algorithm for Support Vector Machines. In: Principe J, Gile L, Morgan N, Wilson E eds., Proceedings of the 1997 IEEE Workshop on Neural Networks for Signal Processing, New York: IEEE, 1997, 276-285
61. Schlkopf B, Sung K K, Burges C, et al. Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans. on Signal Processing, 1997, 45(11): 2758-2765
62. Schlkopf B, Burges C, Vapnik V. Incorporating invariances in support vector learning machines. In: vonder Malsburg C, von Seelen W, Vorbrüggen J C et al (eds), Artificial Neural Networks ICANN'96, Spingers Lecture Notes in Computer Science, Berlin, 1996, 1112: 47-52
63. Guyon I, Matic N, Vapnik V. Discovering informative patterns and data cleaning. In: Fayyad U M, Piatetsky Shapiro G, Smyth P et al (eds), Advances in Knowledge Discovery & Data Mining, MIT Press, 1996, 181-203
64. Lu Chunyu, Yah Pingfan, Zhang Changshui, Zhou Jie. Face recognition using support vector machine. In: Proc. of ICNNB'98, Beijing, 1998, 652-655
65. Osuna E, Freund R, Girosi F. An Improved Training Algorithm for Support Vector Machines. In: Principe J, Gile L, Morgan N, Wilson E eds., Proceedings of the 1997 IEEE Workshop on Neural Networks for Signal Processing, New York: IEEE, 1997, 276-285
66. Anguita D, Ridella S, Rovetta S. Circuital implementation of support vector machines. Electronics Letters, 1998, 34(16): 1596-1597
67. Burges C J C. A Tutorial on Support Vector machines for pattern recognition. Data Mining and Knowledge Discovery. 1998, 2(2): 955-974
68．肖嵘．基于支持向量机的模式识别技术中若干问题的研究．南京大学：博士论文，2001
69．陶卿，姚穗，范劲松．一种新的机器学习算法：Support Vector Machines．模式识别与人工智能，2000，13(3)：285-289
70. Hearst M A, Schlkopf B, Dumais S et al. Trends and controversies support vector machines. IEEE Intelligent Systems, 1998, 13(4): 18-28
71. Smolar A J. Learning with Kernels. PHD Thesis. Technishe University Berlin, 1998
72. G.C. Cawley, N.L.C. Talbot. Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers. Pattern Recognition, 2003, to appear
73. V.N. Vapnik O.Chapelle. Bounds on error expectation for support vector machines. Neural Computerl 2000, 12(9): 2013-2036.
74. S. Mika, G. Rtsch, K.-R. Müller. A mathematical programming approach to the Kernel Fisher algorithm. In T.K. Leen, T.G. Dietterich, V. Tresp, editors, Advances in Neural Information Processing Systems 13, MIT Press, 2001, 591-597
75. S. Mika, A.J. Smola, B. Schlkopf. An improved training algorithm for kernel fisher discriminants. In T. Jaakkola and T. Richardson, editors, Proceedings AISTATS 2001, Morgan Kaufmann, San Francisco, CA, 2001, 98-104
76. S. Mika, G. Rtsch, B. Schlkopf, A. Smola, J. Weston, and K.-R. Müller. Invariant feature extraction and classification in kernel spaces. In Advances in Neural Information Processing Systems 12, Cambridge, MIT Press, MA, 2000, 526-532
77. J. Xu, X. Zhang, Y. Li. Kernel MSE algorithm: a unified framework for KFD, LS-SVM, and KRR. Proceedings of the International Joint Conference on Neural Networks, Washington, De, July 2001, 1486-1491
78. S. A. Billings, K.L Lee. Nonlinear Fisher discriminant analysis using a minimum squared error cost function and the orthogonal least squares algorithm. Neural Networks, 2002, 15(2): 263-270
79. T.V. Gestel, J.A.K. Suykens, G. Lanckriet, A. Lambrechts, B. De Moor, J. Vanderwalle. Bayesian framework for least squares support vector machine classifiers, gaussian processs and kernel fisher discriminant analysis. Neural Computation, 2002, 15(5): 1115-1148
80. N.D. Lawrence, B. Scholkopf. Estimating a kernel Fisher discriminant in the presence of label noise. In Proc. 18th International Conf. on Machine Learning, Morgan Kaufmann, San Francisco, CA, 2001, 306-313
81. Jian Yang, Zhong Jin, Jing-yu Yang, David Zhang. Essence of Kernel Fisher Discriminant: KPCA plus LDA. Pattern Recognition, to appear
82. Yong Xu, Jing-yu Yang, Jian Yang, A Reformative Kernel Fisher Discriminant Analysis, Pattern Recognition, 2004,37: 1299-1302
83. J. Yang, A.F. Frangi, J-y Yang. A New Kernel Fisher Discriminant Algorithm with Application to Face Recognition, Neurocomputing, 2004, 56(1 ):415-21
84. Qingshan Liu, Rui Huang, Hanqing Lu, Songde Ma. Kernel-Based Optinmized Feature Vectors selection and Discriminant Analysis for Face Recognition. In Proceedings of the 16th International Conference on Pattern Recognition, 2002, 2: 362-365
85. K. I. Diamantaras, S.Y.Kung. Principal Component Neural Networks. New York: Woley, 1996
86. S. Mika, B. Scholkopf, A.J. Smola, K. R. Miiller, M. Scholz, G. Ratsch. Kernel PCA and de-noising in feature spaces. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Advances in Neural Information Processing Systems 11, MIT Press, 1999, 536-542
87. Roman. Rosipal, Mark Girolami, Leonard J. Trejo Andrzej Cichocki. Kernal PCA for Feature Extraction and De-Noising in Nonlinear Regression. Neural Computing & Applications, 2001, 10:231-243
88. B. Scholkopf, S. Mika, A.J. Smola, G. Ratsch, and K. R. Miiller. Kernel PCA pattern reconstruction via approximate pre-images. In Proceedings of the 8th International Conference on Artificial Neural Networks, Perspectives in Neural Computing, Berlin, 1998, 147-152
89. A. J. Smola, B. Scholkopf. Sparse greedy matrix approximation for machine learning. In Proc. ICML'00, P. Langley, Ed, San Mateo: Morgan Kaufmann, 2000, 911-918
90. M. Tipping. Sparse kernel principal component analysis. In Advances in Neural Information Processing Systems'13, Cambridge, MA:MIT Press, 2001
91. A.J. Smola, O. L. Mangassarian, B. Sch61kopf. Sparse kemel feature analysis. University of Wisconsin, Data Mining Institute, Madison, Tech. Rep, 1999, 99-04
92. J.A.K. Juykens, T. Van Gestel, J.Vande-walle, B.De Moor. A Support Machine Formulation to Pca Analysis and its Kernel Version. Technical Report, Katholieke Universiteit Leuven, Dapartement of Electrical Engineering, Belgium, 2002-68
93. S. Zhou. Probabilistic analysis of kernel principal components: mixture modeling and classification. CfAR Technical Report, CAR-TR-993, 2003.
94．赵海涛．投影分析在人脸识别中的研究与应用．南京：南京理工大学博士论文，2003
95．高秀梅，杨静宇，杨健．一种最优的核Fisher鉴别分析与人脸识别．系统仿真学报，2004，16(12)
96. Baudat G, Anouar F. Kernel-based Methords and Function Approximation. Washington, DC July, 2001, 1244-1249,
97. Yong Xu, Jing-yu Yang, Jianfeng Lu, Dong-jun Yu. An efficient renovation on kernel Fisher discriminant analysis and face recognition experiments. Pattern Recognition, 2004, 37: 2091-2094
98. Yong Xu, Jing-yu Yang, Zhong Jin. Theory analysis on FSLDA and ULDA. Pattern Recognition, 2003, 36(12): 3031-3033
99. Yuefei Guo, Stan Z. Li, Xiangyang Xue, Lide Wu. A Novel Kernel Discriminant Analysis Method for Dimension Reduction with Application in Face Recognition and Facial Pose Classification. 第四届中国生物识别学术会议，2003，12月，北京
100. Qingshan Liu, Rui Huang, Hanqing Lu, Songde Ma. Face Recognition Using Kernel Based Fisher Discriminant Analysis. Fifth IEEE International Conference on Automatic Face and Gesture Recognition, Washinton D.C, 2002, 20-21
101．陈才扣，宋枫溪，杨静宇．基于图像矩阵的非线性不相关鉴别特征抽取技术．数据采集与处理，2004，19(2)：1-6
102．陈才扣，杨静宇，杨健．一种融合PCA和KFDA的人脸识别方法．控制与决策，2004，19(10)：1147-1150
103. F. Bach, M. I. Jordan. Kernel independent component analysis. Technical Report CSD-01-1166, Computer Science Division, University of California, Berkeley, 2001
104．张莉，周伟达，焦李成．核聚类算法．计算机学报，2002，25(6)：587-590
105. Samal A, Iyengar P A. Automatic recognition and analysis of human faces and facial expressions: a survey. Pattern Recognition, 1992, 25(1): 65-77
106. Chellappa R, Wilson C L, Sirohey S. Human and machine recognition of faces: a survey. Proc. IEEE, 1995, 83(5): 705-740
10
107. Rosenfeld A. Survey: Image analysis and computer vision: 1994. Computer Vision and Image Understanding, 1995, 62(1): 90-143
108. Rosenfeld A. Survey: Image analysis and computer vision: 1996. Computer Vision and Image Understanding, 1997, 66(1): 33-93
109. Maxim A. Grudin. On internal representations in face recognition systems. Pattern Recognition, 2000, 33 (7): 1161-1177
110. A. Pentland. Looking at people: sensing for ubiquitous and wearable computing. IEEE Trans.Pattern Anal. Machine lntell, 2000, 22(1): 107-119
111．周杰，卢春雨，张长水，李衍达．人脸识别方法综述．电子学报，2000，28(4)：102-106
112. Ian Craw, Nicholas Costen, Takashi Kato, et al. How should we represent faces for automatic recognition? IEEE Trans. Pattern Anal. Machine Intell, 1999, 21(8): 725-736
113．刘党辉，沈兰荪，Kin-Man Lam．人脸识别研究进展．电路与系统学报，2004，9(1)：85-94
114. Baron R. Mechanisms of human facial recognition. Int. J. Man-Machine Studies, 1989, 2: 283-310
115. Bruce V. Recognizing faces. London: Erlbaum, 1988
116. Bichsel M. Perceiving and recognizing faces. Mind and Language, 1990, 342-364
117. Ellis H et al. Aspects of face processing. Dordrecht: Nijhoff, 1986
118．荆晓远．模式分类技术在人脸识别中的应用．南京：南京理工大学博士论文，1998
119. Fengxi Song, Jingyu Yang, Shuhai Liu. Large Margin Linear Projection Based and Face Recognition. Pattern Recognition, 2004, 37(9): 1953-1955
120．宋枫溪，程科，杨静宇，刘树海．最大散度差、大间距线性投影与支持向量机．自动化学报，to appear
121. Brunelli R, Poggio T. HyperBF networks for gender classification. Proc. DRRPA, Image Understanding Workshop, 1992, 311-314
122. Sakai T, Nagao M, Fujibayashi. Line extraction and pattern recognition in a photograph. Pattern Recognition, 1969, 1: 233-248
123. Ming-Hsuan Yang, David Kriegman, Narendra Ahuja. Detecting Faces In images: A Survey. IEEE Trans. Pattern Anal. Machine Intell, 2002, 24(1): 34-58
124. Govindaraju V et al. A computional model for face location, Proc. ICCV, 1990, 718-721
125. Sirohey S A. Human face segmentataion and identification. Tech. Rep. CAR-TR-695, Center for Autom. Res. Univ. Maryland, Collehe Park. MD,1993
126．梁路宏，艾海舟，何克忠，张钹．基于多关联模板匹配的人脸检测．软件学报，2001，12(1)，94-102
127. Roudey H A. Neural network-based face detection. Proc. of Image Understanding Workshop, 1996, 725-735
128. Ranganath S, Arun K. Face recognition using transform features and neural networks. Pattern Recognition, 1997, 30(10): 1615-1622.
129. Rowley H A, Baluja S, Kanade T. Neural network-based face detection. IEEE Trans. Pattern Anal. Machine Intell., 1998, 20(1): 23-38
130．杨光正，黄熙涛．镶嵌图在人面定位中的应用．模式识别与人工智能，1996，9(3)：213-220
131. Yang G Z. Human face detection in a complex background. Pattern Recognition, 1994, 27 (1): 53-63.
132．李士进，人脸检测与识别方法研究．南京：南京理工大学博士论文，2000
133. Kelly M D. Visual identification of people by computer.Tech. Rep. AI-130, Stanfort AI Proj., Stanford, CA, 1970.
134. Kanade T. Computer recognition of human faces. Basel and Stuttgart: Birkhauser, 1977
135. Buhr R. Analyze. K-Lassifikation von gesichtsbildern, ntzArchtv, 1986, 8, part 10: 245-256
136. Yuille A, Cohen D. Feature extraction from faces using deformable templates. Proc. IEEE Computer Soc. Conf. on CVPR, 1989, 104-109
137. Craw I. Recognizing face features and faces. IEE Colloquium on Machine Storage and Recognition of Faces, London, Jan. 1992, 71-74
138. Lee S Y, Ham Y K, Park R H. Recognition of human front faces using knowledge-based feature extraction and neuro-fuzzy algorithm. Pattern Recognition, 1996, 29(11): 1863-1876
139．王华，李介谷．人脸斜视图象的特征抽取与恢复．上海交通大学学报，1997，31(1)：101-104
140. Nixon M. Eye spacing measurement for facial recognition. SPIE, 1985, 575: 279-285
141. Daugman J G. High confidence visual recognition of persons by testing statistics independence. IEEE Trans. Pattern Anal. Machine Intell., 1995, 15(11): 1148-1161
142. Lam K M, Yan H. Locating and extracting the eye in human face images. Pattern Recognition, 1996, 29(5): 771-779
143．彭振云，游素亚，徐光佑．允许姿态变化的快速人脸特征检测．中国图象图形学报，1997，2(4)：225-229
144．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
145. Liu K, Cheng Y.-Q, Yang J.-Y., et al.. Algebraic feature extraction for image recognition based on an optimal discriminant criterion. Pattern Recognition, 1993, 26 (6): 903-911
146. Cheng Y Q, Yang J Y et al. Optimal Fisher discriminant analysis using the rank decomposition. Pattern Recognition, 1992, 25(1): 101-111
147. Liu K, Yang J Y et al. A generalized optimal set of discriminant vectors. Pattern Recognition, 1992, 25(7): 731-739
148．郭跃飞，杨静宇．求解广义最佳鉴别矢量集的一种迭代算法及人脸识别．计算机学报，2000，23(11)：1189-1195
149. Jian Yang, Jingyu Yang. What's wrong with the Fisher crierion? Pattern Recognition. 2002, 35(11): 2665-2668
150. Cheng Jun Liu, Harry Wechsler. A shape- and texture-based enhanced Fisher classifier for face recognition. IEEE Trans. Image Processing, 2001, 10(4): 598-608
151．丁学仁，蔡庙可．工程中的矩阵理论．天津：天津大学出版社，1995
152．陈才扣．基于核的非线性特征抽取与图象识别研究．南京：南京理工大学博士论文，2004
153．洪子泉，杨静宇．基于奇异值特征和统计模型的人像识别算法．计算机研究与发展，1994，31(3)：60-65
154．洪子泉．基于代数方法的图象特征抽取和识别．南京：南京理工大学博士论文，1990
155．洪子泉，杨静宇．用于图象识别的图象代数特征抽取．自动化学报，1992，18(2)：232-238
156．Hong Z Q. Algebraic feature extraction of image for recognition. Panem Recognition, 1991, 24(3): 211-219
157．黄修武．基于代数方法的人脸图象特征提取与识别．南京：南京理工大学博士论文，1998
158．黄修武，杨静宇，郭跃飞．基于隶属度的人脸图象特征抽取和识别．电子学报，1998，26(5)：89-92
159. Bartlett M S, Movellan J R, Sejnowski T J. Face Recognition by Independent Component Analysis. IEEE Trans. on Neural Network, 2002, 13(6): 1450-1464
160. Lamarque C H, Robert F. Image analysis using space-filling curves and ID wavelet bases. Pattern Recognition, 1996, 29(8): 1309-1322
161．高西奇，周洪祥，何振亚．基于小波变换的主元分析人脸图象识别．东南大学学报，1996，26(2)：137-141
162. Buhmann J et al. Size and distortion invariant object recognition by hierarchical graph matching, Intel. Conf. on Neural Networks, 1990, 411-416
163. M. Lades, et al. Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Comput., 1993, 42(3): 300-311
164. Wiskott, L. Labeled Graphs and Dynamic Link Matching for Face Recognition and Scene Analysis. PHD thesis. Vol. 53 of Reihe Physik. Verlag Harri Deutsch,Thun, Frankfurt am Main, Germany, 1995
16
165. Kohonen T. Self-organization and associative memory. Berlin: Springer, 1988
166. Ranganath S, Arun K. Face recognition using transform features and neural networks. Pattern Recognition, 1997, 30(10): 1615-1622
167. Lin S H, Kung S Y, Lin L J. Face recognition/detection by probabilistic decision-based neural network. IEEE Trans. on Neural Networks, 1997, 8(1): 114-132
168. Lee S Y, Ham Y K, Park R H. Recognition of human front faces using knowledge-based feature extraction and neuro-fuzzy algorithm. Pattern Recognition, 1996, 29(11): 1863-1876
169. Lawrence S, Giles C L, Tsoi A C, Back A D. Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Network, 1997, 8(1): 98-113
170. G.. Guo, S. Z. Li, Kap Luk Chan. Support vector machines for face recognition. Image and Vision Computing. 2001, 19: 631-638
171. F. S. Samaria. Face recognition using hidden Markov models, PHD thesis, Trinity College, University of Cambridge, Cambridge, 1994
172. 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
173．胡钟山．字符识别技术的研究与应用．南京：南京理工大学博士论文，1999
174．王正群．手写体汉字识别研究．南京：南京理工大学博士论文，2001
175. H. Yoshihiko, et al.. Recognition of handwritten numerals using Gabor features. Proceedings of the Thirteenth ICPR, pp. 250-253.
176. S. X. Liao, M. Pawlak. On image analysis by moments. IEEE Trans. Pattern Anal. Machine Intell, 1996, 18(3): 254-266
177. Bailey R R, Mandyam S. Orthogonal moment feature for use with parametric and non-parametric classifiers. IEEE Trans. Pattern Anal. Machine Intell., 1996, 18(4): 389-398
178. Alireza K, Yawhua H. Invariant image recognition by Zernike moments. IEEE Trans. Pattern Anal. Machine Intell., 1990, 12(5): 489-497
179. Y.T.Tang et al. Offline Recognition of Chinese Handwriting by Multifeature and Multilevel Classification. I EEE Trans. Pattern Anal. Machine lntell, 1998, 20(5): 556-561
180. Y.H.Tseng, C.C.Kuo. H.J.Lee. Speeding Up Chinese Character Recognition in An Automatic Document Reading System. Pattern Recognition, 1998, 31(11): 1601-1612
181．王正群，叶辉等．基于模糊方向特征的手写体汉字识别．模式识别与人工智能，2001，14(3)：41-44
182. Jian Yang, J. Y. Yang. Generalized K-L transformed based combined feature extraction. Pattern Recognition, 2002, 35(1): 295-297
18
183. Yong Xu, Jing-yu Yang, Zhong Jin, Yong Xu, Jing-yu Yang, Zhong jin, A Novel Method For Fisher Discriminant Analysis, Pattern Recognition, 2004, 37: 381-384
184．甘俊英，张有为．模式识别中广义核函数Fisher最佳鉴别．模式识别与人工智能，2002，15(4)：429-433
185. Tian Q, et al. Image classification by the Foley-Sammon transform. Optical Engineering, 1986, 25 (7): 834-839
186．程云鹏．矩阵论．西安：西北工业大学出版社，1989
187．杨健，涂庆华，杨静宇．快速Foley-Sammon鉴别变换及人脸鉴别．中国图象图形学报，2002，7(A)(1)：1-5
188．胡钟山，娄震，杨静宇，刘克，孙靖夷．基于多分类器组合的手写体数字识别．计算机学报，1999，22(4)：369-374
189．吴小俊．图象特征抽取与识别理论及其在人脸识别中的应用．南京：南京理工大学博士论文，2002
190. P.J. Phillips. The Facial recognition Technology(FERET) Database, http://www.itl.nist.go/iad/humanid/feret/feret master.html
191. P.J. Phillips, H.Moons, S. A. Rizvi, P. J. Rauss. The FERET Evaluation Methodology for Face-Recognition Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(10): 1090-1104
192．曾生根，朱宁波，包晔，夏德深．一种改进的快速独立分量分析算法及其在图像分离中的应用．中国图象图形学报，2003，8A(10)：1159-1165
193. Jutten C, Herault J. Independent component analysis verus principal component analysis. In Proc. Europena. Signal Processing Conf, Grenoble, France, 1988, 643-646
194. Comon P. Independent component analysis, a new concept [J]? Signal Process, 1994, 36(3): 287-314
195. Bell A J, Sejnowski T J. An information-maximisation approach to blind separation and blind deconvolution. Neural Computation, 1995, 7(6): 1129-1159
196. Lee T W, et al. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Computation, 1997, 11(2): 417-441
197. Hyvrinen Aapo, Erkki Oja. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Networks, 1999, 8(3): 622-634
198. Hyvrinen Aapo, Erkki Oja.. A fast fixed-point algorithm for independent component analysis. Neural Computation, 1997, 9(7): 1483-1492
199. Hyvrinen Aapo, Erkki Oja.. Independent component analysis: a tutorial. Neural Networks, 2000, 13(45): 411-430
20
200. Bartlett M, Lades H, Sejnowski T. Independent component representations for face recognition. In: Proceedings of the SPIE Symposium on Electronic Imaging: Human Vision and Electronic Imaging. San Jose, California, 1998, 3299-3310
201. Chengjiun Liu, Harry Wechsler. Independent Component Analysis of Gabor Features for Face Recognition. IEEE Transactions on Neural Networks, 2003, 14(4): 919-928
202. Pong C. Yuen, J. H. Lai. Face representation using independent component analysis. Pattern Recognition, 2002, 35(6): 1247-1257
203．曾生根．快速独立分量分析方法及其在图像分析中的若干应用研究．南京：南京理工大学博士论文，2004
204. O.Déniz, M. Castrillón, M. Hernández. Face recognition using independent component analysis and support vector machines. Pattern Recognition Letters, 2003, 24(13): 2153-2157
205. Hoyer P O, Hyvrinen A. Independent component analysis applied to feature extraction from colour and stereo images. Network Computation in Neural Systems; 2000, 11: 191-210
206. Haritopoulos M, Yin H J, Allinson N M. Image denoising using self-orgnizing map-based nonlinear independent component analysis. Neural Networks., 2002, 15(9): 1085-1098
207. Karhunen J, Oja E, Wang L Y, et al. A class of neural networks for independent component analysis. IEEE Trans. on Neural Networks, 1997, 8(3): 486-504
208．杨竹青，李勇，胡德文．独立成分分析方法综述．自动化学报，2002，28(5)：762-772
209. Cover T M, Thomas J A. Elements of Information Theory. New York: Wiley, 1991
210．王宏漫，欧宗瑛．采用PCA／ICA特征和SVM分类的人脸识别．计算集辅助设计与图形学学报，2003，15(4)：416-431
211. Bernd Heisele, Purdy Ho, Tomaso Poggio. Face Recognition with Support Vector Machines: Global versus Component-based Approach. International Conference on Computer Vision (ICCV'01), 2001, 2: 7-14
2．边肇祺，张学工．模式识别(第二版)．北京：清华大学出版社，1999
3. Turk M, Pentland A. Eigenfaces for recognition. J. Cognitive Neuroscience, 1991, 3(1): 71-86
4. Liu K, Yang J.-Y. et al, An efficient algorithm for Foley-Sammon optimal set of discriminant vectors by algebraic method. International Journal of Pattern Recognition and Artificial Intelligence, 1992, 6(5): 817-829
5. Y. F. Guo, T. T, Shu, J. Y. Yang, et al.. Feature extraction method based on the generalized Fisher Discriminant criterion and face recognition. Pattern Analysis & Application, 2001, 4(1): 61-66
6. Peter N. Belhumeur, et al. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans.Pattern Anal. Machine Intell, 1997, 19(7): 711-720
7. B. Schlkopf, S. Mika, C.J.C. Burges, P. Knirsch, K. R. Müller, G. Ritsch, A.J. Smola. Input space vs. feature space in kernel-based methods. IEEE Transactions on Neural Networks, 1999, 10(5): 1000-1017
8. K.-R. Müller, S. Mika, G.Rtsch, K. Tsuda, and B. Schlkopf. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 2001, 12(2): 181-201
9. Colin Campbell. Kernel methods: a survey of current techniques. Neurcomputing, 2002, 48: 63-84
10. Alberto Ruiz, Perdro E. Ldpez-de-Teruel. Nonlinear Kernel-Based Statistical Pattern Analysis. IEEE Transactions on Neural Networks, 2001, 12(1): 1045-9227
11. Vladimir N. Vapnik. The Nature of Statistical Learning Theory. Springer, 1995
12．张学工．关于统计学习理论与支持向量机．自动化学报，2000，26(1)：32-42
13. Cortes C, Vapnik V. Support Vector Networks. Machine Learning, 1995, 20: 273-297
14. Cherkassky V, Mulier F. Learning from Data: Concepts, Theory and Methods. NY: John Viley & Sons, 1997
15. Mika, G. Rtsch, J Weston, B. Schlkopf, A. Smola, and K. R. Müller. Constructing descriptive and discriminative non-linear features: Rayleigh coefficients in kernel feature spaces. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2003, 25(5): 623-628
16. S. Mika, G. Ratsch, J. Weston, B. Scholkopf, K. Muller. Fisher discriminant analysis with kernels. In IEEE Neural Networks for Signal Processing Workshop, 1999, 41-48
17. G. Baudat, F. Anouar. Generalized discriminant analysis using a kernel approach. Neural Computation, 2000, 12: 2385-2404
18. B. Scholkopf, A. Smola, and K.R. Muller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 1998, 10(5): 1299-1319
19. B. Scholkopf, A. Smola, K.R. Muller. Kernel principal component analysis. In W. Gerstner, Artificial Neural Networks - ICANN'97, Berlin, 1997, 583-588
20. Volker Roth, Volker Steinhage. Nonlinear discriminant analysis using kernel functions. In S.A.Solla,T.K.Leen,K.-R.MUller, editors. Advance in Neural Information Processing Systems 12, Cambridge, MA: MIT Press, 2000, 568-574
21. K. I. Kim, K. Jung, H. J. Kim. Face recognition using kernel principal component analysis. IEEE Signal Processing Letters (SPL), 2002, 9(2): 40-42
22. K. I. Kim, S. H. Park, H. J. Kim. Kernel principal component analysis for texture classification. IEEE SPL, 2001, 8(2): 39-41
23. K. I. Kim, K. Jung, S. H. Park, H. J. Kim. Texture classification with kernel principal component analysis. IEE Electronics Letters (EL), 2000, 36(12): 1021-1022
24. M. H. Yang. Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods. Proceedings of the Fifth International Conference on Automatic Face and Gesture Recognition (FG 2002), Washington D. C, May, 2002, 215-220
25. M. H. Yang, N. Ahuja, D. Kriegman. Face recognition using kernel Eigenfaces. In Proceedings of the 2000 IEEE International Conference on Image Processing (ICIP 2000), Vancouver, Canada, September, 2000, 1: 37-40
26. Juwei Lu, K.N. Plataniotis, A.N. Venetsanopoulos. Face Recognition Using Kernel Direct Discriminant Analysis Algorithms. IEEE Transactions on Neural Networks, 2003, 14(1): 117-126
27. C. Liu. Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Trans. Pattern Analysis and Machine Intelligence, 2004, 26(5): 572-581
28. Kirby M, Sirovich L. Application of the KL procedure for the characterization of human faces. IEEE Trans. Pattern Anal. Machine Intell., 1990, 12(1): 103-108
29. A. Pentland, B. Moghaddam, T. Starner. View-based and mododular eigenspaces for face recognition. Proc. IEEE Conf. On Computer Vision and Pattern Recognition, 1994, 84-91
30. Turk M and Pentland A., Face recognition using Eigenfaces, Proc. IEEE Conf. On Computer Vision and Pattern Recognition, 1991, 586-591
31. Bischel M, Pentland A. Human face recognition and face image set's topology. CVGIP: Image Understanding, 1994, 59(2): 54-261.
32. Turk M, Pentland A. Face processing: Models for recognition. Proc. Intelligent Robots and Computer Vision Ⅷ, SPIE, 1989, 1, 192: 22-32
33. R. A. Fisher. The use of multiple measurements in taxonomic problems, Annals of Eugenics 7 (1936) 178-188
34. S. S. Wilks. Mathematical Statistics. Wiley, New York, 1962, 577-578
35. R. Duda, P. Hart. Pattern Classification and Scene Analysis. Wiley, New York, 1973
36. J.W. Sammon. An Optimal discriminant plane. IEEE Trans. Computer, 1970, C-19: 826-829
37. Foley D H, Sammon J W Jr.. An optimal set ofdiscriminant vectors. IEEE Trans. Computer. 1975, 24(3): 281-289
38. Duchene J, Leclercq S. An optimal Transformation for discriminant and principal component analysis. IEEE Trans. Pattern Anal. Machine Intell, 1988, 10(6): 978-983
39. Zhong Jin, J. Y. Yang, Z.S. Hu, Z. Lou. Face Recognition based on uncorrelated discriminant transformation. Pattern Recognition, 2001, 34(7): 1405-1416
40．金忠，杨静宇，陆建峰．一种具有统计不相关性的最优鉴别矢量集．计算机学报，1999，22(10)：1105-1108
41. Z. Jin, J.Y. Yang, Z.M. Tang, Z.S. Hu. A theorem on uncorrelated optimal discriminant vectors. Pattern Recognition, 2001, 34(10): 2041-2047
42．金忠，胡钟山，杨静宇，刘克，孙靖夷．手写体数字有效鉴别特征的抽取和识别．计算机研究与发展，1999，36(12)：1484-1489
43．金忠．人脸图像特征抽取与维数研究．南京：南京理工大学博士论文，1999
44．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．南京：南京理工大学博士论文，2002
45．杨健，杨静宇，金忠．最优鉴别特征的抽取及图像识别．计算机研究与发展，2001，38(11)：1331-1336
46. Daniel L. Swets, John Weng. Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Anal. Machine Intell., 1996, 18(8): 831-836
47. Cheng Jun Liu, Harry Wechsler. Robust coding schemes for indexing and retrieval from large face databases. IEEE Trans. Image Processing, 2000, 9(1): 132-137
48. J. Yang, J-y Yang, A.F. Frangi, D. Zhang. Uncorrelated Projection Discriminant Analysis and Its Application to Face Image Feature Extraction. Int. Journal of Pattern Recognition and Artificial Intelligence, 2003, 17(8): 1325-48
49. J. Yang, J-y Yang, A.F. Frangi. Combined Fisherfaces framework, Image and Vision Computing, 2003, 21 (12): 1037-44
50
50．杨健，杨静宇，叶晖．Fisher线性鉴别分析的理论及其应用．自动化学报，2003，29(4)：481-494
51. Jian Yang, Jing-yu Yang. Why can LDA be performed in PCA transformed space? Pattern Recognition, 2003, 36(2): 563-566
52. Jian Yang, Jing-yu Yang. Optimal FLD algorithm for facial feature extraction. SPIE Proc. Intelligent Robots and Computer Vision ⅩⅩ: Algorithms, techniques, and Active Vision, October, 2001, 4572: 438-444
53. Hong Z. Q., Yang J.Y. et al.. Optimal discriminant plane for a small number of samples and design method of classifier on the plane. Pattern Recognition, 1991, 24(4): 317-324
54. Li-Fen Chert, H.-Y. Mark Liao, Ja-Chen Lin, et al.. Why recognition in statistics-based face recognition system should based on pure face portion: a probabilistic decision-based proof. Pattern Recognition, 2001, 34: 317-324
55. Li-Fen Chert, H.-Y. Mark Liao, M.-T. Ko, et al.. A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognition, 2000, 33(10): 1713-1726
56．郭跃飞．人脸图像的代数特征抽取与最佳鉴别矢量的研究．南京：南京理工大学博士论文，2000
57．郭跃飞，黄修武，杨静宇等．一种求解Fisher最佳鉴别矢量的新方法及人脸识别．中国图象图形学报，1999，4(A)(2)：95-98
58. Hua Yu, Jie Yang. A direct LDA algorithm for high-dimensional data—with application to face recognition. Pattern Recognition, 2001, 34(11): 2067-2070
59. Vapnik V, Levin E, Le Cun Y. Measuring the VC dimension of a learning machine. Neural Computation, 1994, 6: 851-876
60. Osuna E, Freund R, Girosi F. An Inproved Training Algorithm for Support Vector Machines. In: Principe J, Gile L, Morgan N, Wilson E eds., Proceedings of the 1997 IEEE Workshop on Neural Networks for Signal Processing, New York: IEEE, 1997, 276-285
61. Schlkopf B, Sung K K, Burges C, et al. Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans. on Signal Processing, 1997, 45(11): 2758-2765
62. Schlkopf B, Burges C, Vapnik V. Incorporating invariances in support vector learning machines. In: vonder Malsburg C, von Seelen W, Vorbrüggen J C et al (eds), Artificial Neural Networks ICANN'96, Spingers Lecture Notes in Computer Science, Berlin, 1996, 1112: 47-52
63. Guyon I, Matic N, Vapnik V. Discovering informative patterns and data cleaning. In: Fayyad U M, Piatetsky Shapiro G, Smyth P et al (eds), Advances in Knowledge Discovery & Data Mining, MIT Press, 1996, 181-203
64. Lu Chunyu, Yah Pingfan, Zhang Changshui, Zhou Jie. Face recognition using support vector machine. In: Proc. of ICNNB'98, Beijing, 1998, 652-655
65. Osuna E, Freund R, Girosi F. An Improved Training Algorithm for Support Vector Machines. In: Principe J, Gile L, Morgan N, Wilson E eds., Proceedings of the 1997 IEEE Workshop on Neural Networks for Signal Processing, New York: IEEE, 1997, 276-285
66. Anguita D, Ridella S, Rovetta S. Circuital implementation of support vector machines. Electronics Letters, 1998, 34(16): 1596-1597
67. Burges C J C. A Tutorial on Support Vector machines for pattern recognition. Data Mining and Knowledge Discovery. 1998, 2(2): 955-974
68．肖嵘．基于支持向量机的模式识别技术中若干问题的研究．南京大学：博士论文，2001
69．陶卿，姚穗，范劲松．一种新的机器学习算法：Support Vector Machines．模式识别与人工智能，2000，13(3)：285-289
70. Hearst M A, Schlkopf B, Dumais S et al. Trends and controversies support vector machines. IEEE Intelligent Systems, 1998, 13(4): 18-28
71. Smolar A J. Learning with Kernels. PHD Thesis. Technishe University Berlin, 1998
72. G.C. Cawley, N.L.C. Talbot. Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers. Pattern Recognition, 2003, to appear
73. V.N. Vapnik O.Chapelle. Bounds on error expectation for support vector machines. Neural Computerl 2000, 12(9): 2013-2036.
74. S. Mika, G. Rtsch, K.-R. Müller. A mathematical programming approach to the Kernel Fisher algorithm. In T.K. Leen, T.G. Dietterich, V. Tresp, editors, Advances in Neural Information Processing Systems 13, MIT Press, 2001, 591-597
75. S. Mika, A.J. Smola, B. Schlkopf. An improved training algorithm for kernel fisher discriminants. In T. Jaakkola and T. Richardson, editors, Proceedings AISTATS 2001, Morgan Kaufmann, San Francisco, CA, 2001, 98-104
76. S. Mika, G. Rtsch, B. Schlkopf, A. Smola, J. Weston, and K.-R. Müller. Invariant feature extraction and classification in kernel spaces. In Advances in Neural Information Processing Systems 12, Cambridge, MIT Press, MA, 2000, 526-532
77. J. Xu, X. Zhang, Y. Li. Kernel MSE algorithm: a unified framework for KFD, LS-SVM, and KRR. Proceedings of the International Joint Conference on Neural Networks, Washington, De, July 2001, 1486-1491
78. S. A. Billings, K.L Lee. Nonlinear Fisher discriminant analysis using a minimum squared error cost function and the orthogonal least squares algorithm. Neural Networks, 2002, 15(2): 263-270
79. T.V. Gestel, J.A.K. Suykens, G. Lanckriet, A. Lambrechts, B. De Moor, J. Vanderwalle. Bayesian framework for least squares support vector machine classifiers, gaussian processs and kernel fisher discriminant analysis. Neural Computation, 2002, 15(5): 1115-1148
80. N.D. Lawrence, B. Scholkopf. Estimating a kernel Fisher discriminant in the presence of label noise. In Proc. 18th International Conf. on Machine Learning, Morgan Kaufmann, San Francisco, CA, 2001, 306-313
81. Jian Yang, Zhong Jin, Jing-yu Yang, David Zhang. Essence of Kernel Fisher Discriminant: KPCA plus LDA. Pattern Recognition, to appear
82. Yong Xu, Jing-yu Yang, Jian Yang, A Reformative Kernel Fisher Discriminant Analysis, Pattern Recognition, 2004,37: 1299-1302
83. J. Yang, A.F. Frangi, J-y Yang. A New Kernel Fisher Discriminant Algorithm with Application to Face Recognition, Neurocomputing, 2004, 56(1 ):415-21
84. Qingshan Liu, Rui Huang, Hanqing Lu, Songde Ma. Kernel-Based Optinmized Feature Vectors selection and Discriminant Analysis for Face Recognition. In Proceedings of the 16th International Conference on Pattern Recognition, 2002, 2: 362-365
85. K. I. Diamantaras, S.Y.Kung. Principal Component Neural Networks. New York: Woley, 1996
86. S. Mika, B. Scholkopf, A.J. Smola, K. R. Miiller, M. Scholz, G. Ratsch. Kernel PCA and de-noising in feature spaces. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Advances in Neural Information Processing Systems 11, MIT Press, 1999, 536-542
87. Roman. Rosipal, Mark Girolami, Leonard J. Trejo Andrzej Cichocki. Kernal PCA for Feature Extraction and De-Noising in Nonlinear Regression. Neural Computing & Applications, 2001, 10:231-243
88. B. Scholkopf, S. Mika, A.J. Smola, G. Ratsch, and K. R. Miiller. Kernel PCA pattern reconstruction via approximate pre-images. In Proceedings of the 8th International Conference on Artificial Neural Networks, Perspectives in Neural Computing, Berlin, 1998, 147-152
89. A. J. Smola, B. Scholkopf. Sparse greedy matrix approximation for machine learning. In Proc. ICML'00, P. Langley, Ed, San Mateo: Morgan Kaufmann, 2000, 911-918
90. M. Tipping. Sparse kernel principal component analysis. In Advances in Neural Information Processing Systems'13, Cambridge, MA:MIT Press, 2001
91. A.J. Smola, O. L. Mangassarian, B. Sch61kopf. Sparse kemel feature analysis. University of Wisconsin, Data Mining Institute, Madison, Tech. Rep, 1999, 99-04
92. J.A.K. Juykens, T. Van Gestel, J.Vande-walle, B.De Moor. A Support Machine Formulation to Pca Analysis and its Kernel Version. Technical Report, Katholieke Universiteit Leuven, Dapartement of Electrical Engineering, Belgium, 2002-68
93. S. Zhou. Probabilistic analysis of kernel principal components: mixture modeling and classification. CfAR Technical Report, CAR-TR-993, 2003.
94．赵海涛．投影分析在人脸识别中的研究与应用．南京：南京理工大学博士论文，2003
95．高秀梅，杨静宇，杨健．一种最优的核Fisher鉴别分析与人脸识别．系统仿真学报，2004，16(12)
96. Baudat G, Anouar F. Kernel-based Methords and Function Approximation. Washington, DC July, 2001, 1244-1249,
97. Yong Xu, Jing-yu Yang, Jianfeng Lu, Dong-jun Yu. An efficient renovation on kernel Fisher discriminant analysis and face recognition experiments. Pattern Recognition, 2004, 37: 2091-2094
98. Yong Xu, Jing-yu Yang, Zhong Jin. Theory analysis on FSLDA and ULDA. Pattern Recognition, 2003, 36(12): 3031-3033
99. Yuefei Guo, Stan Z. Li, Xiangyang Xue, Lide Wu. A Novel Kernel Discriminant Analysis Method for Dimension Reduction with Application in Face Recognition and Facial Pose Classification. 第四届中国生物识别学术会议，2003，12月，北京
100. Qingshan Liu, Rui Huang, Hanqing Lu, Songde Ma. Face Recognition Using Kernel Based Fisher Discriminant Analysis. Fifth IEEE International Conference on Automatic Face and Gesture Recognition, Washinton D.C, 2002, 20-21
101．陈才扣，宋枫溪，杨静宇．基于图像矩阵的非线性不相关鉴别特征抽取技术．数据采集与处理，2004，19(2)：1-6
102．陈才扣，杨静宇，杨健．一种融合PCA和KFDA的人脸识别方法．控制与决策，2004，19(10)：1147-1150
103. F. Bach, M. I. Jordan. Kernel independent component analysis. Technical Report CSD-01-1166, Computer Science Division, University of California, Berkeley, 2001
104．张莉，周伟达，焦李成．核聚类算法．计算机学报，2002，25(6)：587-590
105. Samal A, Iyengar P A. Automatic recognition and analysis of human faces and facial expressions: a survey. Pattern Recognition, 1992, 25(1): 65-77
106. Chellappa R, Wilson C L, Sirohey S. Human and machine recognition of faces: a survey. Proc. IEEE, 1995, 83(5): 705-740
10
107. Rosenfeld A. Survey: Image analysis and computer vision: 1994. Computer Vision and Image Understanding, 1995, 62(1): 90-143
108. Rosenfeld A. Survey: Image analysis and computer vision: 1996. Computer Vision and Image Understanding, 1997, 66(1): 33-93
109. Maxim A. Grudin. On internal representations in face recognition systems. Pattern Recognition, 2000, 33 (7): 1161-1177
110. A. Pentland. Looking at people: sensing for ubiquitous and wearable computing. IEEE Trans.Pattern Anal. Machine lntell, 2000, 22(1): 107-119
111．周杰，卢春雨，张长水，李衍达．人脸识别方法综述．电子学报，2000，28(4)：102-106
112. Ian Craw, Nicholas Costen, Takashi Kato, et al. How should we represent faces for automatic recognition? IEEE Trans. Pattern Anal. Machine Intell, 1999, 21(8): 725-736
113．刘党辉，沈兰荪，Kin-Man Lam．人脸识别研究进展．电路与系统学报，2004，9(1)：85-94
114. Baron R. Mechanisms of human facial recognition. Int. J. Man-Machine Studies, 1989, 2: 283-310
115. Bruce V. Recognizing faces. London: Erlbaum, 1988
116. Bichsel M. Perceiving and recognizing faces. Mind and Language, 1990, 342-364
117. Ellis H et al. Aspects of face processing. Dordrecht: Nijhoff, 1986
118．荆晓远．模式分类技术在人脸识别中的应用．南京：南京理工大学博士论文，1998
119. Fengxi Song, Jingyu Yang, Shuhai Liu. Large Margin Linear Projection Based and Face Recognition. Pattern Recognition, 2004, 37(9): 1953-1955
120．宋枫溪，程科，杨静宇，刘树海．最大散度差、大间距线性投影与支持向量机．自动化学报，to appear
121. Brunelli R, Poggio T. HyperBF networks for gender classification. Proc. DRRPA, Image Understanding Workshop, 1992, 311-314
122. Sakai T, Nagao M, Fujibayashi. Line extraction and pattern recognition in a photograph. Pattern Recognition, 1969, 1: 233-248
123. Ming-Hsuan Yang, David Kriegman, Narendra Ahuja. Detecting Faces In images: A Survey. IEEE Trans. Pattern Anal. Machine Intell, 2002, 24(1): 34-58
124. Govindaraju V et al. A computional model for face location, Proc. ICCV, 1990, 718-721
125. Sirohey S A. Human face segmentataion and identification. Tech. Rep. CAR-TR-695, Center for Autom. Res. Univ. Maryland, Collehe Park. MD,1993
126．梁路宏，艾海舟，何克忠，张钹．基于多关联模板匹配的人脸检测．软件学报，2001，12(1)，94-102
127. Roudey H A. Neural network-based face detection. Proc. of Image Understanding Workshop, 1996, 725-735
128. Ranganath S, Arun K. Face recognition using transform features and neural networks. Pattern Recognition, 1997, 30(10): 1615-1622.
129. Rowley H A, Baluja S, Kanade T. Neural network-based face detection. IEEE Trans. Pattern Anal. Machine Intell., 1998, 20(1): 23-38
130．杨光正，黄熙涛．镶嵌图在人面定位中的应用．模式识别与人工智能，1996，9(3)：213-220
131. Yang G Z. Human face detection in a complex background. Pattern Recognition, 1994, 27 (1): 53-63.
132．李士进，人脸检测与识别方法研究．南京：南京理工大学博士论文，2000
133. Kelly M D. Visual identification of people by computer.Tech. Rep. AI-130, Stanfort AI Proj., Stanford, CA, 1970.
134. Kanade T. Computer recognition of human faces. Basel and Stuttgart: Birkhauser, 1977
135. Buhr R. Analyze. K-Lassifikation von gesichtsbildern, ntzArchtv, 1986, 8, part 10: 245-256
136. Yuille A, Cohen D. Feature extraction from faces using deformable templates. Proc. IEEE Computer Soc. Conf. on CVPR, 1989, 104-109
137. Craw I. Recognizing face features and faces. IEE Colloquium on Machine Storage and Recognition of Faces, London, Jan. 1992, 71-74
138. Lee S Y, Ham Y K, Park R H. Recognition of human front faces using knowledge-based feature extraction and neuro-fuzzy algorithm. Pattern Recognition, 1996, 29(11): 1863-1876
139．王华，李介谷．人脸斜视图象的特征抽取与恢复．上海交通大学学报，1997，31(1)：101-104
140. Nixon M. Eye spacing measurement for facial recognition. SPIE, 1985, 575: 279-285
141. Daugman J G. High confidence visual recognition of persons by testing statistics independence. IEEE Trans. Pattern Anal. Machine Intell., 1995, 15(11): 1148-1161
142. Lam K M, Yan H. Locating and extracting the eye in human face images. Pattern Recognition, 1996, 29(5): 771-779
143．彭振云，游素亚，徐光佑．允许姿态变化的快速人脸特征检测．中国图象图形学报，1997，2(4)：225-229
144．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
145. Liu K, Cheng Y.-Q, Yang J.-Y., et al.. Algebraic feature extraction for image recognition based on an optimal discriminant criterion. Pattern Recognition, 1993, 26 (6): 903-911
146. Cheng Y Q, Yang J Y et al. Optimal Fisher discriminant analysis using the rank decomposition. Pattern Recognition, 1992, 25(1): 101-111
147. Liu K, Yang J Y et al. A generalized optimal set of discriminant vectors. Pattern Recognition, 1992, 25(7): 731-739
148．郭跃飞，杨静宇．求解广义最佳鉴别矢量集的一种迭代算法及人脸识别．计算机学报，2000，23(11)：1189-1195
149. Jian Yang, Jingyu Yang. What's wrong with the Fisher crierion? Pattern Recognition. 2002, 35(11): 2665-2668
150. Cheng Jun Liu, Harry Wechsler. A shape- and texture-based enhanced Fisher classifier for face recognition. IEEE Trans. Image Processing, 2001, 10(4): 598-608
151．丁学仁，蔡庙可．工程中的矩阵理论．天津：天津大学出版社，1995
152．陈才扣．基于核的非线性特征抽取与图象识别研究．南京：南京理工大学博士论文，2004
153．洪子泉，杨静宇．基于奇异值特征和统计模型的人像识别算法．计算机研究与发展，1994，31(3)：60-65
154．洪子泉．基于代数方法的图象特征抽取和识别．南京：南京理工大学博士论文，1990
155．洪子泉，杨静宇．用于图象识别的图象代数特征抽取．自动化学报，1992，18(2)：232-238
156．Hong Z Q. Algebraic feature extraction of image for recognition. Panem Recognition, 1991, 24(3): 211-219
157．黄修武．基于代数方法的人脸图象特征提取与识别．南京：南京理工大学博士论文，1998
158．黄修武，杨静宇，郭跃飞．基于隶属度的人脸图象特征抽取和识别．电子学报，1998，26(5)：89-92
159. Bartlett M S, Movellan J R, Sejnowski T J. Face Recognition by Independent Component Analysis. IEEE Trans. on Neural Network, 2002, 13(6): 1450-1464
160. Lamarque C H, Robert F. Image analysis using space-filling curves and ID wavelet bases. Pattern Recognition, 1996, 29(8): 1309-1322
161．高西奇，周洪祥，何振亚．基于小波变换的主元分析人脸图象识别．东南大学学报，1996，26(2)：137-141
162. Buhmann J et al. Size and distortion invariant object recognition by hierarchical graph matching, Intel. Conf. on Neural Networks, 1990, 411-416
163. M. Lades, et al. Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Comput., 1993, 42(3): 300-311
164. Wiskott, L. Labeled Graphs and Dynamic Link Matching for Face Recognition and Scene Analysis. PHD thesis. Vol. 53 of Reihe Physik. Verlag Harri Deutsch,Thun, Frankfurt am Main, Germany, 1995
16
165. Kohonen T. Self-organization and associative memory. Berlin: Springer, 1988
166. Ranganath S, Arun K. Face recognition using transform features and neural networks. Pattern Recognition, 1997, 30(10): 1615-1622
167. Lin S H, Kung S Y, Lin L J. Face recognition/detection by probabilistic decision-based neural network. IEEE Trans. on Neural Networks, 1997, 8(1): 114-132
168. Lee S Y, Ham Y K, Park R H. Recognition of human front faces using knowledge-based feature extraction and neuro-fuzzy algorithm. Pattern Recognition, 1996, 29(11): 1863-1876
169. Lawrence S, Giles C L, Tsoi A C, Back A D. Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Network, 1997, 8(1): 98-113
170. G.. Guo, S. Z. Li, Kap Luk Chan. Support vector machines for face recognition. Image and Vision Computing. 2001, 19: 631-638
171. F. S. Samaria. Face recognition using hidden Markov models, PHD thesis, Trinity College, University of Cambridge, Cambridge, 1994
172. 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
173．胡钟山．字符识别技术的研究与应用．南京：南京理工大学博士论文，1999
174．王正群．手写体汉字识别研究．南京：南京理工大学博士论文，2001
175. H. Yoshihiko, et al.. Recognition of handwritten numerals using Gabor features. Proceedings of the Thirteenth ICPR, pp. 250-253.
176. S. X. Liao, M. Pawlak. On image analysis by moments. IEEE Trans. Pattern Anal. Machine Intell, 1996, 18(3): 254-266
177. Bailey R R, Mandyam S. Orthogonal moment feature for use with parametric and non-parametric classifiers. IEEE Trans. Pattern Anal. Machine Intell., 1996, 18(4): 389-398
178. Alireza K, Yawhua H. Invariant image recognition by Zernike moments. IEEE Trans. Pattern Anal. Machine Intell., 1990, 12(5): 489-497
179. Y.T.Tang et al. Offline Recognition of Chinese Handwriting by Multifeature and Multilevel Classification. I EEE Trans. Pattern Anal. Machine lntell, 1998, 20(5): 556-561
180. Y.H.Tseng, C.C.Kuo. H.J.Lee. Speeding Up Chinese Character Recognition in An Automatic Document Reading System. Pattern Recognition, 1998, 31(11): 1601-1612
181．王正群，叶辉等．基于模糊方向特征的手写体汉字识别．模式识别与人工智能，2001，14(3)：41-44
182. Jian Yang, J. Y. Yang. Generalized K-L transformed based combined feature extraction. Pattern Recognition, 2002, 35(1): 295-297
18
183. Yong Xu, Jing-yu Yang, Zhong Jin, Yong Xu, Jing-yu Yang, Zhong jin, A Novel Method For Fisher Discriminant Analysis, Pattern Recognition, 2004, 37: 381-384
184．甘俊英，张有为．模式识别中广义核函数Fisher最佳鉴别．模式识别与人工智能，2002，15(4)：429-433
185. Tian Q, et al. Image classification by the Foley-Sammon transform. Optical Engineering, 1986, 25 (7): 834-839
186．程云鹏．矩阵论．西安：西北工业大学出版社，1989
187．杨健，涂庆华，杨静宇．快速Foley-Sammon鉴别变换及人脸鉴别．中国图象图形学报，2002，7(A)(1)：1-5
188．胡钟山，娄震，杨静宇，刘克，孙靖夷．基于多分类器组合的手写体数字识别．计算机学报，1999，22(4)：369-374
189．吴小俊．图象特征抽取与识别理论及其在人脸识别中的应用．南京：南京理工大学博士论文，2002
190. P.J. Phillips. The Facial recognition Technology(FERET) Database, http://www.itl.nist.go/iad/humanid/feret/feret master.html
191. P.J. Phillips, H.Moons, S. A. Rizvi, P. J. Rauss. The FERET Evaluation Methodology for Face-Recognition Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(10): 1090-1104
192．曾生根，朱宁波，包晔，夏德深．一种改进的快速独立分量分析算法及其在图像分离中的应用．中国图象图形学报，2003，8A(10)：1159-1165
193. Jutten C, Herault J. Independent component analysis verus principal component analysis. In Proc. Europena. Signal Processing Conf, Grenoble, France, 1988, 643-646
194. Comon P. Independent component analysis, a new concept [J]? Signal Process, 1994, 36(3): 287-314
195. Bell A J, Sejnowski T J. An information-maximisation approach to blind separation and blind deconvolution. Neural Computation, 1995, 7(6): 1129-1159
196. Lee T W, et al. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Computation, 1997, 11(2): 417-441
197. Hyvrinen Aapo, Erkki Oja. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Networks, 1999, 8(3): 622-634
198. Hyvrinen Aapo, Erkki Oja.. A fast fixed-point algorithm for independent component analysis. Neural Computation, 1997, 9(7): 1483-1492
199. Hyvrinen Aapo, Erkki Oja.. Independent component analysis: a tutorial. Neural Networks, 2000, 13(45): 411-430
20
200. Bartlett M, Lades H, Sejnowski T. Independent component representations for face recognition. In: Proceedings of the SPIE Symposium on Electronic Imaging: Human Vision and Electronic Imaging. San Jose, California, 1998, 3299-3310
201. Chengjiun Liu, Harry Wechsler. Independent Component Analysis of Gabor Features for Face Recognition. IEEE Transactions on Neural Networks, 2003, 14(4): 919-928
202. Pong C. Yuen, J. H. Lai. Face representation using independent component analysis. Pattern Recognition, 2002, 35(6): 1247-1257
203．曾生根．快速独立分量分析方法及其在图像分析中的若干应用研究．南京：南京理工大学博士论文，2004
204. O.Déniz, M. Castrillón, M. Hernández. Face recognition using independent component analysis and support vector machines. Pattern Recognition Letters, 2003, 24(13): 2153-2157
205. Hoyer P O, Hyvrinen A. Independent component analysis applied to feature extraction from colour and stereo images. Network Computation in Neural Systems; 2000, 11: 191-210
206. Haritopoulos M, Yin H J, Allinson N M. Image denoising using self-orgnizing map-based nonlinear independent component analysis. Neural Networks., 2002, 15(9): 1085-1098
207. Karhunen J, Oja E, Wang L Y, et al. A class of neural networks for independent component analysis. IEEE Trans. on Neural Networks, 1997, 8(3): 486-504
208．杨竹青，李勇，胡德文．独立成分分析方法综述．自动化学报，2002，28(5)：762-772
209. Cover T M, Thomas J A. Elements of Information Theory. New York: Wiley, 1991
210．王宏漫，欧宗瑛．采用PCA／ICA特征和SVM分类的人脸识别．计算集辅助设计与图形学学报，2003，15(4)：416-431
211. Bernd Heisele, Purdy Ho, Tomaso Poggio. Face Recognition with Support Vector Machines: Global versus Component-based Approach. International Conference on Computer Vision (ICCV'01), 2001, 2: 7-14