Skip to main content

Eyes Location Using a Neural Network

  • 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:

  • 63 Accesses

Abstract

This paper proposed a neural network based method for eyes location. In our work, face area is first located initially using an illumination invariant face skin model; Then, it is segmented by the combination of image transformation and a competitive Hopfield neural network (CHNN) and facial feature candidates such as eyes, eyebrows and mouth are obtained; Finally, eyes are located by facial features evaluation and validation, which is based on face’s geometrical structures. Experimental results show that our system performs well under not good lighting conditions.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Wong, K.W., Lam, K.M., Siu, W.C.: A Robust Scheme for Live Detection of Human Faces in Color Images. Signal Processing: Image Communication 18(2), 103–114 (2003)

    Article  Google Scholar 

  2. Yu, L., Eizenman, M.: A New Methodology for Determining Point-of-Gaze in Head-Mounted Eye Tracking Systems. IEEE Trans. Biomedical Engineering 51(1), 1765–1773 (2004)

    Article  Google Scholar 

  3. Paperno, E., Semyonov, D.: A New Method for Eye Location Tracking. IEEE Trans. Biomedical Engineering 50(1), 1174–1179 (2003)

    Article  Google Scholar 

  4. Kapoor, A., Picard, R.W.: Real-Time, Fully Automatic Upper Facial Feature Tracking. In: Williams, A.D. (ed.) Fifth IEEE Int. Conf. Automatic Face and Gesture Recognition, pp. 10–15 (2002)

    Google Scholar 

  5. Huang, J., Wechsler, H.: Visual Routines for Eye Location Using Learning and Evolution. IEEE Trans. Evolutionary Computation 4(1), 73–82 (2000)

    Article  Google Scholar 

  6. Yoo, D.H., Chung, M.J.: Non-Intrusive Eye Gaze Estimation without Knowledge of Eye Pose. In: Proc. Sixth IEEE Int. Conf. Automatic Face and Gesture Recognition, pp. 785–790 (2004)

    Google Scholar 

  7. Martinkauppi, B.: Face Color under Varying Illumination-Analysis and Applications. University of Oulu, Finland (2002)

    Google Scholar 

  8. Hannuksela, J.: Facial Feature based Head Tracking and Pose Estimation. University of Oulu, Finland (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Feng, Xy., Yang, Lp., Dang, Z., Pietikäinen, M. (2006). Eyes Location Using a Neural Network. 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_69

Download citation

  • DOI: https://doi.org/10.1007/11760023_69

  • 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