ICIAP 1997: Image Analysis and Processing pp 332-339 | Cite as

Adaptive logic networks for facial feature detection

  • D. O. Gorodnichy
  • W. W. Armstrong
  • X. Li
Session 10: Recognition & Reconstruction
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)

Abstract

The task of automatic facial feature detection in frontal-view, ID-type pictures is considered. Attention is focused on the problem of eye detection. A neural network approach is tested using adaptive logic networks, which are suitable for this problem on account of their high evaluation speed on serial hardware compared to that of more common multilayer perceptrons. We present theoretical reasoning and experimental results. The experiments are carried out with images of different clarity, scale, lighting, orientation and backgrounds.

Keywords

Face Recognition Facial Image Neural Network Approach Sample Pattern Output Scheme 
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.
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References

  1. 1.
    R. Brunelli and T. Poggio Face recognition: Features versus templates, IEEE Trans. on Pattern Analysis and Machine Intelligence, 15(10), pp. 1042–1052, 1993CrossRefGoogle Scholar
  2. 2.
    N. Roeder and X. Li, “Accuracy analysis for facial feature detection”, Pattern Recognition, Vol.29, No.1, pp.143–157, 1996.CrossRefGoogle Scholar
  3. 3.
    D. O. Gorodnichy, A Way to Improve Error Correction Capability of Hopfield Associative Memory in the Case Of Saturation, HELNET 94–95 International Workshop on Neural Networks Proceedings, Vol. 1/11, VU University Press, Amsterdam, pp.198–212, 1996Google Scholar
  4. 4.
    S. Lawrence, C. L. Giles, A. C. Tsoi, A. D. Back. Face Recognition: A Hybrid Neural Network Approach, IEEE Trans. on Neural Networks, special issue on Pattern Recognition, accepted for publication.Google Scholar
  5. 5.
    R. Hutchinson, W. Welsh. Comparison of Neural Networks and Conventional Technoques for Feature Locationin Facial Images, Proc. First IEE International Conference on ANN, pp.201–205, October 1989.Google Scholar
  6. 6.
    J. B. Waite, Training Multi-Layered Perceptron for facial feature location: a case of study, in Neural networks for vision, speech, and natural language. 1st ed. BT telecommunications series; 1. London: Chapman & Hall, 1992.Google Scholar
  7. 7.
    C. C. Hand, Artificial Neural Networks feature detector using Multi resolution Pyramid, ibid.Google Scholar
  8. 8.
    J. M. Vincent, Image Feature Location in Multi-Resolution Images, ibid.Google Scholar
  9. 9.
    R. M. Debenham, The detection of eyes in facial Images Using Radial Basis Fuctions, ibid.Google Scholar
  10. 10.
    W. W. Armstrong, C. Chu, M. M. Thomas, “Using Adaptive Logic Networks to Predict Machine Failure” in Proc. of the 1995 Workshop on Environmental and Energy Applications of Neural Networks”, World Scientific, Richland, USA, pp. 97–107, 1995.Google Scholar
  11. 11.
    W. W. Armstrong, A. Kostov, R. B. Stein, M. M. Thomas, Adaptive Logic Networks in Rehabilitation of Persons With Incomplete Spinal Cord Injury, pp. 154–171, ibid.Google Scholar
  12. 12.
    W.W Armstrong, M.M.Thomas, Adaptive Logic Networks, sect. in C1.8 in Handbook of Neural Computation, E. Fiesler, R. Beale eds, Institute of Physics Publishing and Oxford University Press-USA, 1996, ISBN 0-7503-0312-3 (looseleaf)Google Scholar
  13. 13.
    W. W. Armstrong, M. Thomas et al, The Atree 3.0 Educational Kit with User Guide available via anonymous ftp from ftp.cs.ualberta.ca [129.128.4.241] in pub/ atree/atree3/atree3ek.exe.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • D. O. Gorodnichy
    • 1
  • W. W. Armstrong
    • 1
  • X. Li
    • 1
  1. 1.Dept. of Computing ScienceUniversity of AlbertaEdmontonCanada

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