Skip to main content
SpringerLink
Log in

A Comparison of Classifiers for Real-Time Eye Detection

  • Conference paper
  • First Online:
Artificial Neural Networks — ICANN 2001 (ICANN 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2130))

Included in the following conference series:

Abstract

We describe a system for detecting and tracking human eyes using a digital camera. The system uses the combination of an active illumination scheme to detect eyes and an appearance-based object classifier to weed out spurious detections. We briefly describe the eye-detection mechanism and then we compare the performances of subspace Gaussian classifiers and support vector machines applied to this task.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 189.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):121–167, 1998.

    Article  Google Scholar 

  2. M. A. Hearst. Support vector machines. IEEE Intelligent Systems, pages 18–28, July/August 1998.

    Google Scholar 

  3. R. Kothari and J. Mitchell. Detection of eye locations in unconstrained visual images. In ICIP 96, 1996.

    Google Scholar 

  4. C.H. Marimoto, D. Koons, A. Amir, and M. Flickner. Frame-rate pupil detector and gaze tracker. In ICCV 99 Frame-rate workshop, Corfu, Greece, September 1999.

    Google Scholar 

  5. E. Osuna, R. Freund, and F. Girosi. An improved training algorithm for support vector machines. In J. Principe, L. Gile, N. Morgan, and E. Wilson, editors, Proc. of IEEE Workshop on Neural Networks for Signal Processing, pages 276–285. IEEE Press, New York, 1997.

    Google Scholar 

  6. D. Pavlov, J. Mao, and B. Dom. Scaling-up support vector machines using boosting algorithm. In Proceedings of ICPR 2000, volume 2, pages 219–222, Barcelona, Catalonia, Spain, September 2000.

    Google Scholar 

  7. J. Platt. Fast training of support vector machines using sequential minimal optimization. In B. Scholkopf, C.J.C. Burges, and A.J. Smola, editors, Advances in Kernel Methods-Support Vector Learning, pages 185–208. MIT Press, Cambridge, MA, 1999.

    Google Scholar 

  8. M. Turk and A. Pentland. Eigenfaces for recognition. J. Cogn. Neurosci., 3:71–86, 1991.

    Article  Google Scholar 

  9. V. N. Vapnik. Estimation of Dependences Based on Empirical Data. Springer Verlag, Berlin, 1982.

    MATH  Google Scholar 

  10. V. N. Vapnik. Statistical Learning Theory. John Wiley & Sons, New York, 1998.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IBM Almaden Research Center, 650 Harry Rd., San Jose, CA, USA

    Alex Cozzi, Myron Flickner, Jainchang Mao & Shivakumar Vaithyanathan

Authors
  1. Alex Cozzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Myron Flickner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jainchang Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shivakumar Vaithyanathan
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Mecidal Cybernetics and Artificial Intelligence, University of Vienna, Freyung 6/2, 1010, Vienna, Austria

    Georg Dorffner

  2. Institute for Computer Aided Automation Pattern Recognition and Image Processing Group, Technical University of Vienna, Favoritenstr. 9/1832, 1040, Vienna, Austria

    Horst Bischof

  3. Institut für Statistik, Wirtschaftsuniversität Wien, Augasse 2-6, 1090, Wien, Austria

    Kurt Hornik

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Cozzi, A., Flickner, M., Mao, J., Vaithyanathan, S. (2001). A Comparison of Classifiers for Real-Time Eye Detection. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_137

Download citation

  • DOI: https://doi.org/10.1007/3-540-44668-0_137

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42486-4

  • Online ISBN: 978-3-540-44668-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation