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Face Recognition Using Kernel UDP

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Abstract

UDP has been successfully applied in many fields, finding a subspace that maximizes the ratio of the nonlocal scatter to the local scatter. But UDP can not represent the nonlinear space well because it is a linear method in nature. Kernel methods can otherwise discover the nonlinear structure of the images. To improve the performance of UDP, kernel UDP (a nonlinear vision of UDP) is proposed for face feature extraction and face recognition via kernel tricks in this paper. We formulate the kernel UDP theory and develop a two-stage method to extract kernel UDP features: namely weighted Kernel PCA plus UDP. The experimental results on the FERET and ORL databases show that the proposed kernel UDP is effective.

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References

  1. Zhao W, Chellappa R, Phillips PJ et al (2003) Face recognition: a literature survey. ACM Comput Surv 35(4): 399–459

    Article  Google Scholar 

  2. Di W, Zhang L, Zhang D, Pan Q (2010) Studies on hyperspectral face recognition in visible spectrum with feature band selection. IEEE Trans Syst Man Cybern A 40(6): 1354–1361

    Article  Google Scholar 

  3. Zhang B, Zhang L, Zhang D, Shen L (2010) Directional binary code with application to PolyU near-infrared face database. Pattern Recognit Lett 31(14): 2337–2344

    Article  Google Scholar 

  4. Jain AK, Chandrasekaran B (1982) Dimension and sample size consideration in pattern recognition Practice. In: Krishnaiah PR, Kanal LN (eds) Handbook of statistic. North Holland, Amsterdam

    Google Scholar 

  5. Kirby M, Sirovich L (1990) Application of the KL procedure for the characterization of human faces. IEEE Trans Pattern Anal Mach Intell 12(1): 103–108

    Article  Google Scholar 

  6. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1): 71–86

    Article  Google Scholar 

  7. Liu K, Cheng YQ, Yang JY, Liu X (1992) An efficient algorithm for Foley–Sammon optimal set of discriminant vectors by algebraic methods. J Pattern Recognit Artif Intell 6(5): 817–829

    Article  Google Scholar 

  8. Swets DL, Weng J (1996) Using discriminant eigenfeatures for image retrieval. IEEE Trans Pattern Anal Mach Intell 18(8): 831–836

    Article  Google Scholar 

  9. Belhumeur V, Hespanha J, Kriegman D (1997) Eigenfaces vs Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7): 711–720

    Article  Google Scholar 

  10. Yang J, Yang JY (2003) Why can LDA be performed in PCA transformed space?. Pattern Recognit 36(2): 563–566

    Article  Google Scholar 

  11. Yang M, Zhang Lei (2010) Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary. In: Proceedings of ECCV (6)’2010, pp 448–461

  12. Yang M, Zhang L, Zhang D, Yang J (2010) Metaface learning for sparse representation based face recognition. In: Proceedings of ICIP’2010, pp 1601–1604

  13. Zhang L, Yang M, Feng Z, Zhang D (2010) On the dimensionality reduction for sparse representation based face recognition. In: Proceedings of ICPR’2010, pp 1237–1240

  14. Tao D, Li X, Wu X, Maybank SJ (2009) Geometric mean for subspace selection. IEEE Trans Pattern Anal Mach Intell 31(2): 260–274

    Article  Google Scholar 

  15. Tao D, Li X, Wu X, Maybank SJ (2007) General averaged divergence analysis. In: ICDM2007, pp 302–311

  16. Zhang T, Tao D, Li X, Yang J (2009) Patch alignment for dimensionality reduction. IEEE Trans Knowl Data Eng 21(9): 1299–1313

    Article  Google Scholar 

  17. Zhang T, Tao D, Yang J (2008) Discriminative locality alignment. In: ECCV2008, pp 725–738

  18. Si S, Tao D, Chan L (2009) Transfer discriminative logmaps. PCM2009, vol 5879/2009, pp 131–143

  19. Si S, Tao D, Geng B (2010) Bregman divergence-based regularization for transfer subspace learning. IEEE Trans Knowl Data Eng 22(7): 929–942

    Article  Google Scholar 

  20. Bian W, Tao D (2011) Max–min distance analysis by using sequential SDP relaxation for dimension reduction. IEEE Trans Pattern Anal Mach Intell 33(5): 1037–1050

    Article  Google Scholar 

  21. Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290: 2319–2323

    Article  Google Scholar 

  22. Roweis ST, Saul LK (2000) Nonlinear dimension reduction by locally linear embedding. Science 290: 2323–2326

    Article  Google Scholar 

  23. Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6): 1373–1396

    Article  MATH  Google Scholar 

  24. He X, Yan S, Hu Y, Niyogi P, Zhang H (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3): 328–340

    Article  Google Scholar 

  25. Yan S, Xu D, Zhang B, Zhang H-J (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1): 40–51

    Article  MathSciNet  Google Scholar 

  26. Chen H-T, Chang H-W, Liu T-L (2005) Local discriminant embedding and its variants. In: Proc IEEE conf computer vision and pattern recognition, pp 846–853

  27. Yang J, Zhang D, Yang J-Y, Niu B (2007) Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Trans Pattern Anal Mach Intell 29(4): 650–664

    Article  Google Scholar 

  28. Deng W, Hu J, Guo J, Zhang H, Zhang C (2008) Comments on globally maximizing, locally minimizing: unsupervised discriminant projection with application to face and palm biometrics. IEEE Trans Pattern Anal Mach Intell 30(8): 1503–1504

    Article  Google Scholar 

  29. Guan N, Tao D, Luo Z, Yuan B (2011) Manifold regularized discriminative non-negative matrix factorization with fast gradient descent. IEEE Trans Image Process 20(7): 2030–2048

    Article  Google Scholar 

  30. Zhou T, Tao D, Wu X (2011) Manifold elastic net: a unified framework for sparse dimension reduction. Data Min Knowl Discov 22(3): 340–371

    Article  Google Scholar 

  31. Yang W, Sun C, Zhang L (2011) A multi-manifold discriminant analysis method for image feature extraction. Pattern Recognit 44(8): 1649–1657

    MATH  Google Scholar 

  32. Yang W, Wang J, Ren M et al (2009) Feature extraction based on laplacian bidirectional maximum margin criterion. Pattern Recognit 42(11): 2327–2334

    Article  MATH  Google Scholar 

  33. Müller K-R, Mika S, Rätsch G, Tsuda K, Schölkopf B (2001) An introduction to kernel-based learning algorithms. IEEE Trans Neural Netw 12(2): 181–201

    Article  Google Scholar 

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

    Article  Google Scholar 

  35. Mika S, Rätsch G, Weston J, Schölkopf B, Müller K-R (1999) Fisher discriminant analysis with kernels. In: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No. 98TH8468), pp 41–48

  36. Yang J, Frangi AF, Yang JY, Zhang D (2005) KPCA plus LDA: a complete kernel Fisher discriminant frame work for feature extraction and recognition. IEEE Trans Pattern Anal Mach Intell 27(2): 230–244

    Article  Google Scholar 

  37. Feng G, Hu D, Zhang D, Zhou Z (2006) An alternative formulation of kernel LPP with application to image recognition. Neurocomputing 69(13–15): 1733–1738

    Article  Google Scholar 

  38. Li JB, Pan J-S, Chu SC (2008) Kernel class-wise locality preserving projection. Inform Sci 178(7): 1825–1835

    Article  MATH  Google Scholar 

  39. Hutson V, Pym JS (1980) Applications of functional analysis and operator theory. Academic Press, London

    MATH  Google Scholar 

  40. Weidmann J (1980) Linear operators in hilbert spaces. Springer, New York

    MATH  Google Scholar 

  41. Lancaster P, Tismenetsky M (1985) The theory of matrices. Academic Press, Orlando

    MATH  Google Scholar 

  42. Golub GH, VanLoan CF (1996) Matrix computations. Johns Hopkins University Press, Baltimore

    MATH  Google Scholar 

  43. Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The FERET evaluation methodology for face-recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22(10): 1090–1104

    Article  Google Scholar 

  44. Phillips PJ (2004) The facial recognition technology (FERET) database. http://www.itl.nist.gov/iad/humanid/feret/feret_master.html

  45. Kreyszig E (1978) Introductory functional analysis with applications. Wiley, New York

    MATH  Google Scholar 

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Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, 210096, People’s Republic of China

    Wankou Yang & Changyin Sun

  2. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Jingyu Yang

  3. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    Helen S. Du

  4. Face Aging Group, Department of Computer Science, UNC Wilmington, Wilmington, NC, USA

    Karl Ricanek

Authors
  1. Wankou Yang
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  2. Changyin Sun
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  3. Jingyu Yang
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  4. Helen S. Du
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  5. Karl Ricanek
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Correspondence to Wankou Yang.

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Yang, W., Sun, C., Yang, J. et al. Face Recognition Using Kernel UDP. Neural Process Lett 34, 177–192 (2011). https://doi.org/10.1007/s11063-011-9190-0

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  • DOI: https://doi.org/10.1007/s11063-011-9190-0

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