Advertisement

Improving Radial Basis Function Networks for Human Face Recognition Using a Soft Computing Approach

  • Wanida Pensuwon
  • Rod Adams
  • Neil Davey
  • Wiroj Taweepworadej
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)

Abstract

In this paper, a new efficient method is proposed based on the radial basis function neural networks (RBFNs) architecture for human face recognition system using a soft computing approach. The performance of the present method has been evaluated using the BioID Face Database and compared with traditional radial basis function neural networks. The new approach produces successful results and shows significant recognition error reduction and learning efficiency relative to existing technique.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Bishop, C.M.: Improving the generalization properties of radialbasis function neural networks. Neural Computation 3(4), 579–581 (1991)CrossRefGoogle Scholar
  3. 3.
    Howell, A.J., Buxton, H.: Face recognition using radial basis function neural networks. In: Fisher, R.B., Trucco, E. (eds.) Proc. British Machine Vision Conf., pp. 455–464. BMVA Press, Edinburgh (1996)Google Scholar
  4. 4.
    Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face recognition: a convolutional neural network approach. IEEE Trans. Neural Networks 8, 98–113 (1997)CrossRefGoogle Scholar
  5. 5.
    Leonard, J.A., Kramer, M.A.: Radial basis function networks for classifying process faults. IEEE Control System 11, 31–38 (1991)CrossRefGoogle Scholar
  6. 6.
    Lin, S.H., Kung, S.Y., Lin, L.J.: Face recognition/detection by probabilistic decision-based neural network. IEEE Trans. Neural Networks 8, 114–132 (1997)CrossRefGoogle Scholar
  7. 7.
    Looney, C.G.: Pattern Recognition Using Neural Networks. Oxford University Press, New York (1997)Google Scholar
  8. 8.
    Mao, K.Z.: RBF neural network centre selection based on fisher ratio class separability measure. IEEE Transactions on Neural Networks 13(5), 1211–1217 (2002)CrossRefGoogle Scholar
  9. 9.
    Park, J., Sandberg, I.W.: Approximation and radial basis function networks. Neural Computation 5, 305–316 (1993)CrossRefGoogle Scholar
  10. 10.
    Ryoo, Y.J., Lim, W.C., Kim, K.H.: Classification of materials using temperature response curve fitting and fuzzy neural network. Sensor Actuators A: Physics 94(1–2), 11–18 (2001)CrossRefGoogle Scholar
  11. 11.
    Sarimveis, H., Alexandridis, A., Tsekouras, G., Bafas, G.: A fast and efficient algorithm for training radial basis function neural networks based on a fuzzy partition of the input space. Ind. Eng. Chem. Research 41, 751–759 (2002)CrossRefGoogle Scholar
  12. 12.
    Scholkopf, B., Sung, K.K., Burges, C.J.C., Girosi, F., Niyogi, P., Poggio, T., et al.: Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Transactions on Signal Processing 45(11), 2758–2765 (1997)CrossRefGoogle Scholar
  13. 13.
    Walczak, B., Massart, D.L.: Local modeling with radial basis function networks. Chemometr. Intelligent Laboratory System 50, 179–198 (2000)CrossRefGoogle Scholar
  14. 14.
    Xu, L., Krzyzak, A., Yuille, A.: On radial basis function nets and kernel regression: statistical consistency, convergence rates, and receptive field size. Neural Networks 7(4), 609–628 (1994)zbMATHCrossRefGoogle Scholar
  15. 15.
    Zadeh, L.A.: Fuzzy logic, neural networks and soft computing. One page course announcement of CS 294-4, Spring 1993, the University of California at Berkeley (November 1992)Google Scholar
  16. 16.
    Zadeh, L.A.: Fuzzy logic computing with words. IEEE Transactions on Fuzzy Systems 4, 103–111 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wanida Pensuwon
    • 1
    • 2
  • Rod Adams
    • 2
  • Neil Davey
    • 2
  • Wiroj Taweepworadej
    • 1
  1. 1.Department of Computer EngineeringKhon Kaen UniversityKhon KaenThailand
  2. 2.Department of Computer ScienceUniversity of HertfordshireHatfield, HertsUnited Kingdom

Personalised recommendations