Kernel Manifold Learning-Based Face Recognition

Chapter

Abstract

Feature extraction with dimensionality reduction is an important step and essential process in embedding data analysis. Linear dimensionality reduction aims to develop a meaningful low-dimensional subspace in a high-dimensional input space such as PCA and LDA. LDA is to find the optimal projection matrix with Fisher criterion through considering the class labels, and PCA seeks to minimize the mean square error criterion.

References

  1. 1.
    Hu YC (2007) Fuzzy integral-based perception for two-class pattern classification problems. Inf Sci 177(7):1673–1686CrossRefMATHGoogle Scholar
  2. 2.
    Chen C, Zhang J, Fleischer R (2010) Distance approximating dimension reduction of Riemannian manifolds. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 40(1):208–217CrossRefGoogle Scholar
  3. 3.
    Zhang T, Huang K, Li X, Yang J, Tao D (2010) Discriminative orthogonal neighborhood-preserving projections for classification. IEEE Trans Syst Man Cybern B Cybern 40(1):253–263CrossRefGoogle Scholar
  4. 4.
    Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces versus Fisherfaces: recognition using class specific linear projection. Trans. Pattern Analysis Mach Intell 19(7):711–720CrossRefGoogle Scholar
  5. 5.
    Batur AU, Hayes MH (2001) Linear subspace for illumination robust face recognition. In: Proceedings of IEEE international conference on computer vision and pattern recognition, pp 296–301Google Scholar
  6. 6.
    Hastie T, Stuetzle W (1989) Principal curves. J American Stat Assoc 84(406):502–516MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Chang KY, Ghosh J (2001) A unified model for probabilistic principal surfaces. IEEE Trans Pattern Anal Mach Intell 23(1):22–41CrossRefGoogle Scholar
  8. 8.
    Graepel T, Obermayer K (1999) A stochastic self-organizing map for proximity data. Neural Comput 11(1):139–155CrossRefGoogle Scholar
  9. 9.
    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):1180–1192CrossRefGoogle Scholar
  10. 10.
    Yin H (2002) Data visualization and manifold mapping using the ViSOM. Neural Netw 15(8):1005–1016CrossRefGoogle Scholar
  11. 11.
    Tenenbaum JB, Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323CrossRefGoogle Scholar
  12. 12.
    Roweis ST, Saul LK (2000) Nonlinear dimensionality deduction by locally linear embedding. Science 290(5500):2323–2326CrossRefGoogle Scholar
  13. 13.
    He X, Niyogi P (2003) Locality preserving projections. In: Proceedings of conference in advances in neural information processing systems, pp 585–591Google Scholar
  14. 14.
    He X, Yan S, Hu Y, Niyogi P, Zhang HJ (2007) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340Google Scholar
  15. 15.
    Zheng Z, Yang F, Tan W, Jia J, Yang J (2007) Gabor feature-based face recognition using supervised locality preserving projection. Signal Proces 87(10):2473–2483CrossRefMATHGoogle Scholar
  16. 16.
    Zhu L, Zhu S (2007) Face recognition based on orthogonal discriminant locality preserving projections. Neurocomputing 70(7–9):1543–1546CrossRefGoogle Scholar
  17. 17.
    Cai D, He X, Han J, Zhang HJ (2006) Orthogonal Laplacianfaces for face recognition. IEEE Trans Image Proces 15(11):3608–3614CrossRefGoogle Scholar
  18. 18.
    Yu X, Wang X (2008) Uncorrelated discriminant locality preserving projections. IEEE Signal Process Lett 15:361–364CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    van Gestel T, Baesens B, Martens D (2010) From linear to non-linear kernel based classifiers for bankruptcy prediction. Neurocomputing 73(16–18), pp 2955–2970Google Scholar
  21. 21.
    Zhua Q (2010) Reformative nonlinear feature extraction using kernel MSE. Neurocomputing 73(16–18):3334–3337CrossRefGoogle Scholar
  22. 22.
    Cheng J, Liu Q, Lua H, Chen YW (2005) Supervised kernel locality preserving projections for face recognition. Neurocomputing 67:443–449CrossRefGoogle Scholar
  23. 23.
    Zhao H, Sun S, Jing Z, Yang J (2006) Local structure based supervised feature extraction. Pattern Recogn 39(8):1546–1550CrossRefMATHGoogle Scholar
  24. 24.
    Li JB, Pan JS, Chu SC (2008) Kernel class-wise locality preserving projection. Inf Sci 178(7):1825–1835CrossRefMATHGoogle Scholar
  25. 25.
    Veerabhadrappa M, Rangarajan L (2010) Diagonal and secondary diagonal locality preserving projection for object recognition. Neurocomputing 73(16–18), pp 3328–3333Google Scholar
  26. 26.
    Wang X, Chung F-L, Wang S (2010) On minimum class locality preserving variance support vector machine. Pattern Recogn 43(8):2753–2762CrossRefMATHGoogle Scholar
  27. 27.
    Zhang L, Qiao L, Chen S (2010) Graph-optimized locality preserving projections. Pattern Recogn 43(6):1993–2002CrossRefMATHGoogle Scholar
  28. 28.
    Wang J, Zhang B, Wang S, Qi M, Kong J (2010) An adaptively weighted sub-pattern locality preserving projection for face recognition. J Netw Comput Appl 33(3):323–332CrossRefGoogle Scholar
  29. 29.
    Lu G-F, Lin Z, Jin Z (2010) Face recognition using discriminant locality preserving projections based on maximum margin criterion. Pattern Recogn 43(10):3572–3579CrossRefMATHGoogle Scholar
  30. 30.
    Hu D, Feng G, Zhou Z (2007) Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition. Pattern Recogn 40(1):339–342CrossRefMATHGoogle Scholar
  31. 31.
    Xu Y, Feng G, Zhao Y (2009) One improvement to two-dimensional locality preserving projection method for use with face recognition. Neurocomputing 73(1–3):245–249CrossRefGoogle Scholar
  32. 32.
    Zhi R, Ruan Q (2008) Facial expression recognition based on two-dimensional discriminant locality preserving projections. Neurocomputing 71(7–9):1730–1734CrossRefGoogle Scholar
  33. 33.
    Pan JS, Li JB, Lu ZM (2008) Adaptive quasiconformal kernel discriminant analysis. Neurocomputing 71(13–15):2754–2760CrossRefGoogle Scholar
  34. 34.
    Amari S, Wu S (1999) Improving support vector machine classifiers by modifying kernel functions. Neural Netw 12(6):783–789CrossRefGoogle Scholar
  35. 35.
    Li JB, Pan JS, Chen SM (2007) Adaptive class-wise 2DKPCA for image matrix based feature extraction. In: Proceedings of the 12th conference on artificial intelligence and applications, Taiwan, pp 1055–1061Google Scholar
  36. 36.
    Xiong H, Swamy MN, Ahmad MO (2005) Optimizing the kernel in the empirical feature space. IEEE Trans Neural Netw 16(2):460–474CrossRefGoogle Scholar
  37. 37.
    Mika S, Ratsch G, Weston J, Schölkopf B, Muller KR (1999) Fisher discriminant analysis with kernels. In: Proceedings of IEEE international workshop neural networks for signal processing 4:41–48Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Automatic Test and ControlHarbin Institute of TechnologyHarbinPeople’s Republic of China
  2. 2.School of Information and EngineeringFlinders University of South AustraliaBedford ParkAustralia
  3. 3.HIT Shenzhen Graduate SchoolHarbin Institute of TechnologyShenzhen CityPeople’s Republic of China

Personalised recommendations