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Face Verification Based on Bagging RBF Networks

  • Yunhong Wang
  • Yiding Wang
  • Anil K. Jain
  • Tieniu Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

Face verification is useful in a variety of applications. A face verification system is vulnerable not only to variations in ambient lighting, facial expression and facial pose, but also to the effect of small sample size during the training phase. In this paper, we propose an approach to face verification based on Radial Basis Function (RBF) networks and bagging. The technique seeks to offset the effect of using a small sample size during the training phase. The RBF networks are trained using all available positive samples of a subject and a few randomly selected negative samples. Bagging is then applied to the outputs of these RBF-based classifiers. Theoretical analysis and experimental results show the validity of the proposed approach.

Keywords

Radial Basis Function Face Image Radial Basis Function Neural Network Radial Basis Function Network Verification System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Kotropoulos, C.L., Wilson, C.L., Sir Ohey, S.: Human and Machine Recognition of Faces: a Survey. Proc. IEEE 83(5), 705–741 (1995)CrossRefGoogle Scholar
  2. 2.
    Liu, X., Chen, T., Vijaya Kumar, B.V.K.: Face authentication for multiple subjects using eigenflow. Pattern Recognition 36(2), 313–328 (2003)CrossRefGoogle Scholar
  3. 3.
    Marcialis, G.l., Roli, F.: Fusion of LDA and PCA for Face Verification. In: Tistarelli, M., Bigun, J., Jain, A.K. (eds.) ECCV 2002. LNCS, vol. 2359, pp. 30–37. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
  5. 5.
    Wang, Y., Tan, T., Zhu, Y.: Face Verification Based on Singular Value Decomposition and Radial Basis Function Neural Network. In: Proceedings of Asian Conference on Computer Vision, ACCV, pp. 432–436 (2002)Google Scholar
  6. 6.
    Er, M.J., Wu, S., Lu, J., Toh, H.L.: Face Recognition With Radial Basis Function (RBF) Neural Networks. IEEE Trans. on NN 13(3), 697–710 (2002)Google Scholar
  7. 7.
    Skurichina, M., Duin, R.P.W.: Bagging, Boosting and the Random Subspace Method for Linear Classifiers. Pattern Analysis & Applications 5, 121–135 (2002)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Samaria, F., Harter, A.: Parameterization of a Stochastic Model for Human Face Identification. In: Proc. 2nd IEEE workshop on Applications of Computer Vision, Sarasota, FL (1994)Google Scholar
  9. 9.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  10. 10.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. MacMillan Publishing Company, Basingstoke (1994)MATHGoogle Scholar
  11. 11.

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yunhong Wang
    • 1
  • Yiding Wang
    • 2
  • Anil K. Jain
    • 3
  • Tieniu Tan
    • 4
  1. 1.School of Computer Science and EngineeringBeihang UniversityBeijingChina
  2. 2.Graduate SchoolChinese Academy of SciencesBeijingChina
  3. 3.Department of Computer Science & EngineeringMichigan State UniversityEast Lansing
  4. 4.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingP.R. China

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