A Regularized Margin Fisher Analysis Method for Face Recognition

  • Xiaoyu Xue
  • Xiaohu Ma
  • Yuxin Gu
  • Xiao Sun
  • Zhiwen Ni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10639)

Abstract

Margin Fisher Analysis is a typical graph-based dimensionality reduction technique and has been successfully applied to face recognition. However, it always suffers from the over-fitting, noise, and singular matrix problems. Common preprocessing methods such as PCA lose certain discriminant information in data, which leads the poor classification rate. We propose a novel method called Regularized Margin Fisher Analysis, which decomposes the inter-class similarity matrix into three subspace: principal space, noise space and null space. Then, we regularize the three subspaces in different ways to deal with the noise and over-fitting problems. Moreover, we use twice standard eigendecompositions instead of single generalized eigendecomposition which avoids the singular matrix problem. The experiments on Extended YaleB, CMU PIE and FERET face databases demonstrates that the proposed method is effective and can improve the classification ability.

Keywords

Face recognition Graph embedding Dimensionality reduction Regularization Margin fisher analysis 

Notes

Acknowledgments

This work is partially supported by the National Natural Science Foundation of China (61402310). Natural Science Foundation of Jiangsu Province of China (BK20141195).

References

  1. 1.
    Basri, R., Jacobs, D.W.: Lambertian reflectance and linear subspaces. In: Proceedings of the Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 2, pp. 383–390 (2003)Google Scholar
  2. 2.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)CrossRefGoogle Scholar
  3. 3.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: NIPS, vol. 14, pp. 585–591 (2001)Google Scholar
  4. 4.
    Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)CrossRefGoogle Scholar
  5. 5.
    He, X., Cai, D., Yan, S., Zhang, H.J.: Neighborhood preserving embedding. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1208–1213. IEEE (2005)Google Scholar
  6. 6.
    He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.J.: Face recognition using Laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 328–340 (2005)CrossRefGoogle Scholar
  7. 7.
    Jiang, X., Mandal, B., Kot, A.: Eigenfeature regularization and extraction in face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 383–394 (2008)CrossRefGoogle Scholar
  8. 8.
    Lee, K.C., Ho, J., Kriegman, D.J.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 684–698 (2005)CrossRefGoogle Scholar
  9. 9.
    Liu, Q., Lu, H., Ma, S.: Improving kernel fisher discriminant analysis for face recognition. IEEE Trans. Circ. Syst. Video Technol. 14(1), 42–49 (2004)CrossRefGoogle Scholar
  10. 10.
    Niyogi, X.: Locality preserving projections. In: Neural Information Processing Systems, vol. 16, p. 153. MIT (2004)Google Scholar
  11. 11.
    Pang, Y.H., Teoh, A.B.J., San Hiew, F.: Locality regularization embedding for face verification. Pattern Recogn. 48(1), 86–102 (2015)CrossRefGoogle Scholar
  12. 12.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The feret evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2002)CrossRefGoogle Scholar
  13. 13.
    Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression (PIE) database. In: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, pp. 46–51 (2002)Google Scholar
  14. 14.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)CrossRefGoogle Scholar
  15. 15.
    Yan, S., Xu, D., Zhang, B., Zhang, H.J., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 40–51 (2007)CrossRefGoogle Scholar
  16. 16.
    Yang, J., Frangi, A.F., Yang, J.Y., Zhang, D., Jin, Z.: KPCA plus LDA: a complete kernel fisher discriminant framework for feature extraction and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 27(2), 230–44 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Xiaoyu Xue
    • 1
    • 2
  • Xiaohu Ma
    • 1
    • 2
  • Yuxin Gu
    • 1
    • 2
  • Xiao Sun
    • 1
    • 2
  • Zhiwen Ni
    • 1
    • 2
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjingChina

Personalised recommendations