Skip to main content
Springer Nature Link
Log in

Face Recognition Using a Neural Network Simulating Olfactory Systems

  • Conference paper
Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3972))

Included in the following conference series:

Abstract

A novel chaotic neural network K-set has been constructed based in research on biological olfactory systems. This non-convergent neural network simulates the capacities of biological brains for signal processing in pattern recognition. Its accuracy and efficiency are demonstrated in this report on an application to human face recognition, with comparisons of performance with conventional pattern recognition algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Freeman, W.J., Kozma, R.: Biocomplexity: Adaptive Behavior in Complex Stochastic Dynamic Systems. Biosystems 59, 109–123 (2001)

    Article  Google Scholar 

  2. Li, G., Lou, Z., Wang, L., Li, X., Freeman, W.J.: Application of Chaotic Neural Model Based on Olfactory System on Pattern Recognitions. In: Wang, L., Chen, K., Ong, Y. S. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 378–381. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  3. Pan, Z., Adams, R., Bolouri, H.: Dimensionality Reduction of Face Images Using Discrete Cosine Transforms for Recognition. IEEE Conference on Computer Vision and Pattern Recognition (2000)

    Google Scholar 

  4. Samaria, F.: Face Recognition Using Hidden Markov Models, PhD Thesis, Cambridge University (1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027, China

    Guang Li

  2. Department of Biomedical Engineering, Zhejiang University, Hangzhou, 310027, China

    Jin Zhang & You Wang

  3. Software College, Human University, Changsha, 410082, China

    Jin Zhang

  4. Division of Neurobiology, University of California at Berkeley, Donner 101, Berkeley, CA, 94720-3206, USA

    Walter J. Freeman

Authors
  1. Guang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. You Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Walter J. Freeman
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Li, G., Zhang, J., Wang, Y., Freeman, W.J. (2006). Face Recognition Using a Neural Network Simulating Olfactory Systems. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_14

Download citation

  • DOI: https://doi.org/10.1007/11760023_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics