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Introduction

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Kernel Learning Algorithms for Face Recognition

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Abstract

Face recognition (FR) has become a popular research topic in the computer vision, image processing, and pattern recognition areas. Recognition performance of the practical FR system is largely influenced by the variations in illumination conditions, viewing directions or poses, facial expression, aging, and disguises. FR provides the wide applications in commercial, law enforcement, military, and so on, such as airport security and access control, building surveillance and monitoring, human–computer intelligent interaction and perceptual interfaces, smart environments at home, office, and cars.

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References

  1. Duda RO, Hart PE, Stork DG (2000) Pattern classification. Wiley-Interscience Publication, New York

    Google Scholar 

  2. Bishop CM (2006) Pattern recognition and machine learning. Springer, New York

    Google Scholar 

  3. Bishop C (2005) Neural networks for pattern recognition. Oxford University Press, Oxford

    Google Scholar 

  4. Vapnik V (1982) Estimation of dependences based on empirical data. Springer, New York

    Google Scholar 

  5. Tan P, Steinbach M, Kumar V (2005) Introduction to data mining. Pearson Addison Wesley, Boston

    Google Scholar 

  6. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140

    MathSciNet  MATH  Google Scholar 

  7. Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Proceedings of the international conference on machine learning, pp 148–156

    Google Scholar 

  8. Freund Y (1995) Boosting a weak learning algorithm by majority. Inf Comput 121(2):256–285

    Article  MathSciNet  MATH  Google Scholar 

  9. Berkhin P (2006) A survey of clustering data mining techniques, Chap. 2. Springer, pp 25–71

    Google Scholar 

  10. Ball G, Hall D (1965) ISODATA, a novel method of data analysis and pattern classification. Technical Report NTIS AD 699616, Stanford Research Institute, Stanford, CA

    Google Scholar 

  11. Lloyd S (1982) Least squares quantization in pcm. IEEE Trans Inf Theory 28:129–137

    Article  MathSciNet  MATH  Google Scholar 

  12. McLachlan GL, Basford KE (1987) Mixture models: inference and applications to clustering. Marcel Dekker

    Google Scholar 

  13. McLachlan GL, Peel D (2000) Finite mixture models. Wiley

    Google Scholar 

  14. Figueiredo M, Jain A (2002) Unsupervised learning of finite mixture models. IEEE Trans Pattern Anal Mach Intell, pp 381–396

    Google Scholar 

  15. Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022

    MATH  Google Scholar 

  16. Teh Y, Jordan M, Beal M, Blei D (2006) Hierarchical Dirichlet processes. J Am Stat Assoc 101(476):1566–1581

    Article  MathSciNet  MATH  Google Scholar 

  17. Blei DM, Jordan MI (2004) Hierarchical topic models and the nested Chinese restaurant process. Adv Neural Inf Process Syst

    Google Scholar 

  18. Rasmussen CE (2000) The infinite gaussian mixture model. Adv Neural Inf Process Syst, pp 554–560

    Google Scholar 

  19. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22:888–905

    Article  Google Scholar 

  20. Ng AY, Jordan MI, Weiss Y (2001) On spectral clustering: analysis and an algorithm. Adv Neural Inf Process Syst, pp 849–856

    Google Scholar 

  21. Li Z, Liu J, Chen S, Tang X (2007) Noise robust spectral clustering. In: Proceedings of the international conference on computer vision, pp 1–8

    Google Scholar 

  22. Robbins H, Monro S (1951) A stochastic approximation method. Ann Math Stat 22:400–407

    Article  MathSciNet  MATH  Google Scholar 

  23. Vapnik V, Sterin A (1977) On structural risk minimization or overall risk in a problem of pattern recognition. Autom Remote Control 10(3):1495–1503

    Google Scholar 

  24. Bengio Y, Alleau OB, Le Roux N (2006) Label propagation and quadratic criterion. In: Chapelle O, Schölkopf B, Zien A (eds) Semi-supervised learning. MIT Press, pp 193–216

    Google Scholar 

  25. Szummer M, Jaakkola T (2001) Partially labeled classification with Markov random walks. Adv Neural Inf Process Syst, pp 945–952

    Google Scholar 

  26. Blum A, Chawla S (2001) Learning from labeled and unlabeled data using graph mincuts. In: Proceedings of the international conference on machine learning, pp 19–26

    Google Scholar 

  27. Joachims T (2003) Transductive learning via spectral graph partitioning. In: Proceedings of the international conference on machine learning, pp 290–297

    Google Scholar 

  28. Chapelle O, Zien A (2005) Semi-supervised classification by low density separation. In: Proceedings of the international conference on artificial intelligence and statistics, pp 57–64

    Google Scholar 

  29. Chapelle O, Scholkopf B, Zien A (eds) (2006) Semi-supervised learning. MIT Press

    Google Scholar 

  30. Joachims T (1999) Transductive inference for text classification using support vector machines. In: Proceedings of the international conference on machine learning, pp 200–209

    Google Scholar 

  31. Fung G, Mangasarian O (2001) Semi-supervised support vector machines for unlabeled data classification. Optim Methods Softw 15:29–44

    Article  MATH  Google Scholar 

  32. Muller KR, Mika S, Ratsch G, Tsuda K, Scholkopf B (2001) An introduction to kernel-based learning algorithms. IEEE Trans Neural Networks 12(2):181–201

    Article  Google Scholar 

  33. Campbell C (2002) Kernel methods: a survey of current techniques. Neurocomputing 48:63–84

    Article  MATH  Google Scholar 

  34. Ruiz A, Lopez-de-Teruel PE (2001) Nonlinear Kernel-based statistical pattern analysis. IEEE Trans Neural Networks 12(1):1045–1052

    Article  Google Scholar 

  35. Mika S, Ratsch G, Weston J, Scholkopf B, Muller K (1999) Fisher discriminant analysis with kernels. IEEE neural networks for signal processing workshop, pp 41–48

    Google Scholar 

  36. Baudat G, Anouar F (2000) Generalized discriminant analysis using a kernel approach. Neural Comput 12:2385–2404

    Article  Google Scholar 

  37. Scholkopf B, Smola A, Muller KR (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299–1319

    Article  Google Scholar 

  38. Mika S, Ratsch G, Weston J, Scholkopf B, Smola A, Muller KR (2003) Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature space. IEEE Trans Pattern Anal Mach Intell 25(5):623–628

    Google Scholar 

  39. Vapnik VN (2000) The nature of statistical learning theory, 2nd edn. Springer, NY

    Google Scholar 

  40. Vapnik VN (1995) The nature of statistical learning theory. Springer

    Google Scholar 

  41. Scholkopf B, Smola A, Muller KR (1996) Nonlinear component analysis as a kernel eigenvalue problem. Technical Report No. 44. Max-Planck-Institut fur biologische Kybernetik, Tubingen Neural Computation 10(5):1299–1319

    Google Scholar 

  42. Scholkopf B, Smola A, Muller KR (1997) Kernel principal component analysis. In: Gerstner W (ed) Artificial neural networks, pp 583–588

    Google Scholar 

  43. Scholkopf B, Mika S, Burges CJC, Knirsch P, Muller KR, Ratsch G, Smola AJ (1999) Input space vs. feature space in kernel-based methods. IEEE Trans Neural Networks 10(5):1000–1017

    Article  Google Scholar 

  44. Mika S, Ratsch G, Weston J, Scholkopf B, Smola A, Muller KR (2003) Constructing descriptive and discriminative non-linear feature: Rayleigh coefficients in kernel feature space. IEEE Trans Pattern Anal Mach Intell 25(5):623–628

    Google Scholar 

  45. Mika S, Ratsch G, Muller KR (2001) A mathematical programming approach to the Kernel Fisher algorithm. In Leen TK, Dietterich TG, Tresp V (eds) Advances in neural information processing systems. MIT Press

    Google Scholar 

  46. Mika S, Smola A, Scholkopf B (2001) An improved training algorithm for kernel fisher discriminants. In: Jaakkola T, Richardson T (eds) Proceedings AISTATS, pp 98–104

    Google Scholar 

  47. Mika S, Scholkopf B, Smola AJ, Muller KR, Scholz M, Ratsch G (1999) Kernel PCA and de-noising in feature spaces. In: Kearns MS, Solla SA, Cohn DA (eds) Advances in neural information processing systems, vol 11, pp 536–542

    Google Scholar 

  48. Baudat G, Anouar F (2000) Generalized discriminant analysis using a kernel approach. Neural Comput 12:2385–2404

    Article  Google Scholar 

  49. Roth V, Steinhage V (1999) Nonlinear discriminant analysis using kernel functions. In: Proceedings of neural information processing systems, Denver, Nov 1999

    Google Scholar 

  50. Wang T-S, Zheng N-N, Li Y, Xu Y-Q, Shum H-Y (2003) Learning kernel-based HMMs for dynamic sequence synthesis. Graph Models 65:206–221

    Article  Google Scholar 

  51. Zhang B-L, Zhang H, Sam Ge S (2004) Face recognition by applying wavelet subband representation and kernel associative memory. IEEE Trans Neural Networks 15(1):166–177

    Google Scholar 

  52. Amari S, Wu S (1999) Improving support vector machine classifiers by modifying kernel functions. Neural Network 12(6):783–789

    Article  Google Scholar 

  53. Peng J, Heisterkamp DR, Dai HK (2004) Adaptive quasiconformal kernel nearest neighbor classification. IEEE Trans Pattern Anal Mach Intell 26(5):656–661

    Google Scholar 

  54. Jiao L, Li Q (2006) Kernel matching pursuit classifier ensemble. Pattern Recogn 39:587–594

    Google Scholar 

  55. Zhang D, Chen S, Zhou Z-H (2006) Learning the kernel parameters in kernel minimum distance classifier. Pattern Recogn 39:133–135

    Article  MATH  Google Scholar 

  56. Jiao L, Li Q (2006) Kernel matching pursuit classifier ensemble. Pattern Recogn 39:587–594

    Article  MATH  Google Scholar 

  57. Ma B, Qu H-y, Wong H-s (2007) Kernel clustering-based discriminant analysis. Pattern Recogn 40(1):324–327

    Article  MATH  Google Scholar 

  58. Szymkowiak Have A, Girolami MA, Larsen J (2006) Clustering via kernel decomposition. IEEE Trans Neural Networks 17(1):256–264

    Google Scholar 

  59. Kima D-W, Young Lee K, Lee D, Lee KH (2005) Evaluation of the performance of clustering algorithms in kernel-induced feature space. Pattern Recogn 38(4):607–611

    Google Scholar 

  60. Yang J, Yang J-y, Frangi AF (2003) Combined Fisherfaces framework. Image Vis Comput 21:1037–1044

    Article  Google Scholar 

  61. Liu C (2004) Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Trans Pattern Anal Mach Intell 26(5):572–581

    Article  Google Scholar 

  62. Lu J, Plataniotis KN, Venetsanopoulos AN (2003) Face recognition using kernel direct discriminant analysis algorithms. IEEE Trans Neural Networks 14(1):117–226

    Article  Google Scholar 

  63. Lu J, Plataniotis KN, Venetsanopoulos AN (2003) Face recognition using kernel direct discriminant analysis algorithms. IEEE Trans Neural Networks 14(1):117–126

    Article  Google Scholar 

  64. Liang Z, Shi P (2005) Kernel direct discriminant analysis and its theoretical foundation. Pattern Recogn 38:445–447

    Article  MATH  Google Scholar 

  65. Liang Y, Li C, Gong W, Pan Y (2007) Uncorrelated linear discriminant analysis based on weighted pairwise Fisher criterion. Pattern Recogn 40:3606–3615

    Article  MATH  Google Scholar 

  66. Liang Z, Shi P (2005) Uncorrelated discriminant vectors using a kernel method. Pattern Recogn 38:307–310

    Google Scholar 

  67. Yang J, Frangi AF, Yang J-y, Zhang D, Jin Z (2005) KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition. IEEE Trans Pattern Anal Mach Intell 27(2):230–244

    Article  Google Scholar 

  68. Belhumeur V, Hespanda J, Kiregeman D (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans PAMI 19:711–720

    Google Scholar 

  69. Yang J, Jin Z, Yang J-y, Zhang D, Frangi AF (2004) Essence of kernel Fisher discriminant: KPCA plus LDA. Pattern Recogn 37:2097–2100

    Article  Google Scholar 

  70. Baudat G, Anouar F (2000) Generalized discriminant analysis using a kernel approach. Neural Comput 12(10):2385–2404

    Article  Google Scholar 

  71. Zheng W, Zou C, Zhao L (2005) Weighted maximum margin discriminant analysis with kernels. Neurocomputing 67:357–362

    Article  Google Scholar 

  72. Wu X-H, Zhou J-J (2006) Fuzzy discriminant analysis with kernel methods. Pattern Recogn 39(11):2236–2239

    Google Scholar 

  73. Tao D, Tang X, Li X, Rui Y (2006) Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm. IEEE Trans Multimedia 8(4):716–727

    Article  Google Scholar 

  74. Huang J, Yuen PC, Chen W-S, Lai JH (2004) Kernel subspace LDA with optimized kernel parameters on face recognition. In: Proceedings of the 6th IEEE international conference on automatic face and gesture recognition, pp 1352–1355

    Google Scholar 

  75. Wang L, Chan KL, Xue P (2005) A criterion for optimizing kernel parameters in KBDA for image retrieval. IEEE Trans Syst Man Cybern B Cybern 35(3):556–562

    Google Scholar 

  76. Chen W-S, Yuen PC, Huang J, Dai D-Q (2005) Kernel machine-based one-parameter regularized Fisher discriminant method for face recognition. IEEE Trans Syst Man Cybern B Cybern 35(4):658–669

    Google Scholar 

  77. Liang Z, Shi P (2004) Efficient algorithm for kernel discriminant analysis. Pattern Recogn 37(2):381–384

    Article  Google Scholar 

  78. Liang Z, Shi P (2004) An efficient and effective method to solve kernel Fisher discriminant analysis. Neurocomputing 61:485–493

    Article  Google Scholar 

  79. Yang MH (2002) Kernel Eigenfaces vs. Kernel Fisherfaces: face recognition using kernel methods. In: Proceedings of fifth IEEE international conference on automatic face and gesture recognition, pp 215–220

    Google Scholar 

  80. Zheng Y-j, Yang J, Yang J-y, Wu X-j (2006) A reformative kernel Fisher discriminant algorithm and its application to face recognition. Neurocomputing 69(13):1806–1810

    Article  Google Scholar 

  81. Xu Y, Zhang D, Jin Z, Li M, Yang J-Y (2006) A fast kernel-based nonlinear discriminant analysis for multi-class problems. Pattern Recogn 39(6):1026–1033

    Article  MATH  Google Scholar 

  82. Saadi K, Talbot NLC, Cawley GC (2007) Optimally regularised kernel Fisher discriminant classification. Neural Networks 20(7):832–841

    Article  MATH  Google Scholar 

  83. Yeung D-Y, Chang H, Dai G (2007) Learning the kernel matrix by maximizing a KFD-based class separability criterion. Pattern Recogn 40(7):2021–2028

    Article  MATH  Google Scholar 

  84. Shen LL, Bai L, Fairhurst M (2007) Gabor wavelets and general discriminant analysis for face identification and verification. Image Vis Comput 25(5):553–563

    Article  Google Scholar 

  85. Ma B, Qu H-y, Wong H-s (2007) Kernel clustering-based discriminant analysis. Pattern Recogn 40(1):324–327

    Article  MATH  Google Scholar 

  86. Liu Q, Lu H, Ma S (2004) Improving kernel Fisher discriminant analysis for face recognition. IEEE Trans Pattern Anal Mach Intell 14(1):42–49

    Google Scholar 

  87. Wang T-S, Zheng N-N, Li Y, Xu Y-Q, Shum H-Y (2003) Learning kernel-based HMMs for dynamic sequence synthesis. Graph Models 65:206–221

    Article  Google Scholar 

  88. Yang J, Gao X, Zhang D, Yang J-y (2005) Kernel ICA: an alternative formulation and its application to face recognition. Pattern Recogn 38:1784–1787

    Google Scholar 

  89. Chang C, Ansari R (2005) Kernel particle filter for visual tracking. IEEE Signal Process Lett 12(3):242–245

    Article  Google Scholar 

  90. Zhang P, Peng J, Domeniconi C (2005) Kernel pooled local subspaces for classification. IEEE Trans Syst Man Cybern 35(3):489–542

    Article  Google Scholar 

  91. Zhang B-L, Zhang H, Sam Ge S (2004) Face recognition by applying wavelet subband representation and kernel associative memory. IEEE Trans Neural Networks 15(1):166–177

    Google Scholar 

  92. Zhu Z, He H, Starzyk JA, Tseng C (2007) Self-organizing learning array and its application to economic and financial problems. Inf Sci 177(5):1180–1192

    Article  Google Scholar 

  93. Mulier F, Cherkassky V (1995) Self-organization as an iterative kernel smoothing process. Neural Comput 7:1165–1177

    Article  Google Scholar 

  94. Ritter H, Martinetz T, Schulten K (1992) Neural computation and self-organizing maps: an introduction. Addison-Wesley, Reading

    MATH  Google Scholar 

  95. Zhang H, Zhang B, Huang W, Tian Q (2005) Gabor wavelet associative memory for face recognition. IEEE Trans Neural Networks 16(1):275–278

    Article  Google Scholar 

  96. Sussner P (2003) Associative morphological memories based on variations of the kernel and dual kernel methods. Neural Networks 16:625–632

    Article  Google Scholar 

  97. Wang M, Chen S (2005) Enhanced FMAM based on empirical kernel map. IEEE Trans Neural Networks 16(3):557–564

    Google Scholar 

  98. LeCun Y, Jackel LD, Bottou L, Brunot A, Corts C, Denker JS, Drucker H, Guyon I, Muller UA, Sackinger E, Simard P, Vapnik V (1995) Comparison of learning algorithms for handwritten digit recognition. In: Proceedings of international conferences on artificial neural networks, vol 2, pp 53–60

    Google Scholar 

  99. Scholkopf B (1997) Support vector learning. Oldenbourg-Verlag, Munich

    Google Scholar 

  100. Yang MH (2002) Kernel eigenfaces vs. kernel Fisherfaces: face recognition using kernel methods. In: Proceedings of the fifth international conferences on automatic face and gesture recognition, pp 1425–1430

    Google Scholar 

  101. Kim KI, Jung K, Kim HJ (2002) Face recognition using kernel principal component analysis. IEEE Signal Process Lett 9(2):40–42

    Article  Google Scholar 

  102. Pang S, Kim D, Bang SY (2003) Membership authentication in the dynamic group by face classification using SVM ensemble. Pattern Recogn Lett 24:215–225

    Article  MATH  Google Scholar 

  103. Joachims T (1998) Text categorization with support vector machines. In: Proceedings of European conferences on machine learning, pp 789–794

    Google Scholar 

  104. Leopold E, Kindermann J (2002) Text categorization with support vector machines. How to represent texts in input space? Machine Learning 46:423–444

    Article  MATH  Google Scholar 

  105. Pearson WR, Wood T, Zhang Z, Miller W (1997) Comparison of DNA sequences with protein sequences. Genomics 46(1):24–36

    Article  Google Scholar 

  106. Hua S, Sun Z (2001) Support vector machine approach for protein subcellular localization prediction. Bioinformatics 17(8):721–728

    Article  Google Scholar 

  107. Hua S, Sun Z (2001) A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. J Mol Biol 308:397–407

    Article  Google Scholar 

  108. Fyfe C, Corchado J (2002) A comparison of kernel methods for instantiating case based reasoning systems. Adv Eng Inform 16:165–178

    Article  Google Scholar 

  109. Heisterkamp DR, Peng J, Dai HK (2001) Adaptive quasiconformal kernel metric for image retrieval. In: Proceedings of CVPR (2), pp 388–393

    Google Scholar 

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Authors and Affiliations

  1. Department of Automatic Test and Control, Harbin Institute of Technology, Yi-Kuang Street 2, Harbin, People’s Republic of China

    Jun-Bao Li

  2. School of Information and Engineering, Flinders University of South Australia, Sturt Road, Bedford Park, SA, 5042, Australia

    Shu-Chuan Chu

  3. HIT Shenzhen Graduate School, Harbin Institute of Technology, Room 424, Building D, Shenzhen Graduate School of Harbin Institute of Technology, Xili University Town, Nanshan,, Shenzhen City, Guangdong Province, People’s Republic of China

    Jeng-Shyang Pan

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  1. Jun-Bao Li
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  2. Shu-Chuan Chu
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  3. Jeng-Shyang Pan
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Correspondence to Jun-Bao Li .

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Li, JB., Chu, SC., Pan, JS. (2014). Introduction. In: Kernel Learning Algorithms for Face Recognition. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0161-2_1

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