Neural Processing Letters

, 34:177 | Cite as

Face Recognition Using Kernel UDP

  • Wankou Yang
  • Changyin Sun
  • Jingyu Yang
  • Helen S. Du
  • Karl Ricanek


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.


UDP Kernel Feature extraction Face Recognition 


  1. 1.
    Zhao W, Chellappa R, Phillips PJ et al (2003) Face recognition: a literature survey. ACM Comput Surv 35(4): 399–459CrossRefGoogle Scholar
  2. 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): 1354–1361CrossRefGoogle Scholar
  3. 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): 2337–2344CrossRefGoogle Scholar
  4. 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, AmsterdamGoogle Scholar
  5. 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): 103–108CrossRefGoogle Scholar
  6. 6.
    Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1): 71–86CrossRefGoogle Scholar
  7. 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): 817–829CrossRefGoogle Scholar
  8. 8.
    Swets DL, Weng J (1996) Using discriminant eigenfeatures for image retrieval. IEEE Trans Pattern Anal Mach Intell 18(8): 831–836CrossRefGoogle Scholar
  9. 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): 711–720CrossRefGoogle Scholar
  10. 10.
    Yang J, Yang JY (2003) Why can LDA be performed in PCA transformed space?. Pattern Recognit 36(2): 563–566CrossRefGoogle Scholar
  11. 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 448–461Google Scholar
  12. 12.
    Yang M, Zhang L, Zhang D, Yang J (2010) Metaface learning for sparse representation based face recognition. In: Proceedings of ICIP’2010, pp 1601–1604Google Scholar
  13. 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 1237–1240Google Scholar
  14. 14.
    Tao D, Li X, Wu X, Maybank SJ (2009) Geometric mean for subspace selection. IEEE Trans Pattern Anal Mach Intell 31(2): 260–274CrossRefGoogle Scholar
  15. 15.
    Tao D, Li X, Wu X, Maybank SJ (2007) General averaged divergence analysis. In: ICDM2007, pp 302–311Google Scholar
  16. 16.
    Zhang T, Tao D, Li X, Yang J (2009) Patch alignment for dimensionality reduction. IEEE Trans Knowl Data Eng 21(9): 1299–1313CrossRefGoogle Scholar
  17. 17.
    Zhang T, Tao D, Yang J (2008) Discriminative locality alignment. In: ECCV2008, pp 725–738Google Scholar
  18. 18.
    Si S, Tao D, Chan L (2009) Transfer discriminative logmaps. PCM2009, vol 5879/2009, pp 131–143Google Scholar
  19. 19.
    Si S, Tao D, Geng B (2010) Bregman divergence-based regularization for transfer subspace learning. IEEE Trans Knowl Data Eng 22(7): 929–942CrossRefGoogle Scholar
  20. 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): 1037–1050CrossRefGoogle Scholar
  21. 21.
    Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290: 2319–2323CrossRefGoogle Scholar
  22. 22.
    Roweis ST, Saul LK (2000) Nonlinear dimension reduction by locally linear embedding. Science 290: 2323–2326CrossRefGoogle Scholar
  23. 23.
    Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6): 1373–1396MATHCrossRefGoogle Scholar
  24. 24.
    He X, Yan S, Hu Y, Niyogi P, Zhang H (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3): 328–340CrossRefGoogle Scholar
  25. 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): 40–51MathSciNetCrossRefGoogle Scholar
  26. 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 846–853Google Scholar
  27. 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): 650–664CrossRefGoogle Scholar
  28. 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): 1503–1504CrossRefGoogle Scholar
  29. 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): 2030–2048CrossRefGoogle Scholar
  30. 30.
    Zhou T, Tao D, Wu X (2011) Manifold elastic net: a unified framework for sparse dimension reduction. Data Min Knowl Discov 22(3): 340–371CrossRefGoogle Scholar
  31. 31.
    Yang W, Sun C, Zhang L (2011) A multi-manifold discriminant analysis method for image feature extraction. Pattern Recognit 44(8): 1649–1657MATHGoogle Scholar
  32. 32.
    Yang W, Wang J, Ren M et al (2009) Feature extraction based on laplacian bidirectional maximum margin criterion. Pattern Recognit 42(11): 2327–2334MATHCrossRefGoogle Scholar
  33. 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): 181–201CrossRefGoogle Scholar
  34. 34.
    Schölkopf B, Smola A, Müller KR (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5): 1299–1319CrossRefGoogle Scholar
  35. 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 41–48Google Scholar
  36. 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): 230–244CrossRefGoogle Scholar
  37. 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): 1733–1738CrossRefGoogle Scholar
  38. 38.
    Li JB, Pan J-S, Chu SC (2008) Kernel class-wise locality preserving projection. Inform Sci 178(7): 1825–1835MATHCrossRefGoogle Scholar
  39. 39.
    Hutson V, Pym JS (1980) Applications of functional analysis and operator theory. Academic Press, LondonMATHGoogle Scholar
  40. 40.
    Weidmann J (1980) Linear operators in hilbert spaces. Springer, New YorkMATHGoogle Scholar
  41. 41.
    Lancaster P, Tismenetsky M (1985) The theory of matrices. Academic Press, OrlandoMATHGoogle Scholar
  42. 42.
    Golub GH, VanLoan CF (1996) Matrix computations. Johns Hopkins University Press, BaltimoreMATHGoogle Scholar
  43. 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): 1090–1104CrossRefGoogle Scholar
  44. 44.
    Phillips PJ (2004) The facial recognition technology (FERET) database.
  45. 45.
    Kreyszig E (1978) Introductory functional analysis with applications. Wiley, New YorkMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2011

Authors and Affiliations

  • Wankou Yang
    • 1
  • Changyin Sun
    • 1
  • Jingyu Yang
    • 2
  • Helen S. Du
    • 3
  • Karl Ricanek
    • 4
  1. 1.School of AutomationSoutheast UniversityNanjingPeople’s Republic of China
  2. 2.School of Computer Science and TechnologyNanjing University of Science and TechnologyNanjingPeople’s Republic of China
  3. 3.Department of ComputingThe Hong Kong Polytechnic UniversityHong KongHong Kong
  4. 4.Face Aging Group, Department of Computer ScienceUNC WilmingtonWilmingtonUSA

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