Advertisement

Enhanced Audio-Visual Recognition System over Internet Protocol

  • Yee Wan WongEmail author
  • Kah Phooi Seng
  • Li-Minn Ang
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 52)

Abstract

In this chapter, we extend the research work in Wong [1] and enhance the audio-visual recognition system over internet protocol. The multiband feature fusion method is presented to solve the illumination problem. Then a radial basis function neural network with a new orthogonal least square algorithm is proposed to improve the generalization of the radial basis function neural network with conventional orthogonal least square algorithm. Result shows that the proposed neural network achieves higher recognition accuracy with lesser number of neurons as compared to the neural network with conventional orthogonal least square algorithm. With this neural network, the recognition accuracy of the audio-visual recognition system is improved as compared to the audio-visual recognition system in Wong [1]. Then the audio-visual recognition system over internet protocol is developed where the multiband feature fusion and the proposed neural network are implemented.

Keywords

Audio-visual recognition system Neural network Illumination variation Internet protocol Multiband feature fusion 

References

  1. 1.
    Wong, Y.W., Seng, K.P., & Ang, L.-M. (2009). Audio-visual recognition system insusceptible to illumination variation over Internet Protocol. IAENG Internation Journal of Computer Science, 36(2), IJCS_36_2_08.Google Scholar
  2. 2.
    Primer, A. (1998). Introduction to wavelet and wavelet transform. Prentice Hall.Google Scholar
  3. 3.
    Wong, Y.W., Seng, K.P., & Ang, L.-M. (Mar 2009). Audio-visual authentication system over the Internet Protocol. Proceedings of the International MultiConference of Engineers and Computer Scientists 2009 (IMECS 2009). The 2009 IAENG International Conference on Imaging Engineering (ICIE’09), I, 938–943.Google Scholar
  4. 4.
    Campbell, J.P., Jr. (Sept 1997). Speaker recognition: A tutorial. Proceedings of IEEE, 85(9), 1437–1462.CrossRefGoogle Scholar
  5. 5.
    Wong, Y.W., Seng, K.P., Ang, L.-M., Khor, W.Y., & Liau, H.F. (Dec 2007). Audio-visual recognition system with intra-modal fusion. 2007 International Conference on Computational Intelligence and Security, pp. 609–613.Google Scholar
  6. 6.
  7. 7.
  8. 8.
    Belhumeur P., & Kriegman, D. (1998). What is the set of images of an object under all possible lighting conditions. International Journal of Computer Vision, 28, 245–260.CrossRefGoogle Scholar
  9. 9.
    Lee, K.-C., Ho, J., & Kriegman, D.J. (May 2005). Acquiring linear subspaces for face recognition under variable lighting illuminations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5), 684–698.CrossRefGoogle Scholar
  10. 10.
    Christopher M. Bishop (2003). Neural networks for pattern recognition. Oxford University Press, pp. 116–163.Google Scholar
  11. 11.
    Williams P.M. (1995). Bayesian regularisation and pruning using a Laplace prior. Neural Computation, 7(1), 117–143.CrossRefGoogle Scholar
  12. 12.
    Feng, G.C., Yuen, P.C., & Dai, D.Q. (Apr 2000). Human face recognition using PCA on wavelet subband. Journal of Electronic Imaging, 9(2), 226–233.CrossRefGoogle Scholar
  13. 13.
    Swets D.L., & Weng, J. (Aug 1996). Using discriminant eigenfeatures for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8), 831–836.CrossRefGoogle Scholar
  14. 14.
    Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86.CrossRefGoogle Scholar
  15. 15.
    Bartlett, M.S., Movellan, J.R., & Sejnowski, T.J. (Nov 2002). Face recognition by independent component analysis. IEEE Transactions on Neural Networks, 13, 1450–1464.CrossRefGoogle Scholar
  16. 16.
    Martinez A.M., & Benavente, R. (1998). The AR face database. CVC Tech. Report #24.Google Scholar
  17. 17.
    Patterson, E.K., Gurbuz, S., Tufekci, Z., & Gowdy, J.N. (2002). CUAVE: A new audio-visual database for multimodal human-computer interface. In Proceedings ICASSP, pp. 2017–2020.Google Scholar
  18. 18.
    Jain, L.C., Halici, U., Hayashi, I., Lee, S.B., & Tsutsui, S. (1999). Intelligent biometric techniques in fingerprint and face recognition. Boca Raton, FL: CRC Press,.Google Scholar
  19. 19.
    Er, M.J., Wu, S., Lu, J., & Toh, H.L. (May 2002). Face recognition with radial basis function (RBF) neural networks. IEEE Transactions on Neural Networks, 13(3), 697–710.CrossRefGoogle Scholar
  20. 20.
    Wu, L., & Moody, J. (1996). A smoothing regularizer for feedforward and recurrent neural networks. Neural Computers, 8, 461–489.zbMATHCrossRefGoogle Scholar
  21. 21.
    Chen, S., Cowan, C.C.N., & Grant, P.M. (1991). Orthogonal least squares for radial basis function networks. IEEE Transactions on Neural Network, 2(2), 302–309.CrossRefGoogle Scholar
  22. 22.
    Chen, S., Chng, E.S., & Alkadhimi, K. (1996). Regularized orthogonal least squares algorithm for constructing radial basis function networks. Internetional Journal of Control, 64(5), 829–837.MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.The University of Nottingham Malaysia CampusSemenyihMalaysia

Personalised recommendations