Skip to main content

Robust Face-Tracking Using Skin Color and Facial Shape

  • Conference paper
  • First Online:
Audio- and Video-Based Biometric Person Authentication (AVBPA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2688))

Abstract

This paper proposes a robust face tracking algorithm based on the CONDENSATION algorithm that uses skin color and facial shape as observation measures. Two independent trackers are used for robust tracking: one is tracking for the skin colored region and another is tracking for the facial shape region. The two trackers are coupled by using an importance sampling technique, where the skin color density obtained from the skin color tracker is used as the importance function to generate samples for the shape tracker. The samples of the skin color tracker within the chosen shape region are updated with higher weights. The proposed face tracker shows a robust tracking performance over the skin color based tracker or the facial shape based tracker given the presence of clutter background and/or illumination changes.

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. Yang, J., Waibel A.: A Real-Time Face Tracker. Proceeding of WACV.(1996) 142–147.

    Google Scholar 

  2. Qian R. J., Sezan M. I., Matthews K.E.: A Robust Real-Time Face Tracking Algorithm. Int. Conf. on Image Processing.(1998) 131–135.

    Google Scholar 

  3. Jang G. J., Kweon I. S.: Robust Real-time Face Tracking Using Adaptive Color Model. International Symposium on Mechatronics and Intelligent Mechanical System for 21 Century, Changwon, Korea.(2000)

    Google Scholar 

  4. Raja Y., McKenna S. J., Gong S.: Colour Model Selection and Adaptation in Dynamic Scenes, In 5th European Conference on Computer Vision. (1998) 460–474.

    Google Scholar 

  5. Birchfield S.: An Elliptical Head Tracker. 31st Asilomar Conference. (1997) 1710–1714.

    Google Scholar 

  6. Birchfield S.: Elliptical Head Tracking Using Intensity Gradients and Color Histograms. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, California. (1998) 232–237.

    Google Scholar 

  7. Isard M., Blake A.: CONDENSATION-conditional density propagation for visual tracking. International Journal of Computer Vision 29(1). (1998) 5–28.

    Article  Google Scholar 

  8. Isard M., Blake A.: ICONDENSATION: Unifying low-level and high-level tracking in a stochastic framework. ECCV (1). (1998) 893–908.

    Google Scholar 

  9. Nishihara H.K., Thomas H. J., Huber E.: Real-time tracking of people using stereo and motion. Proceedings of the SPIE, volume 2183. (1994) 266–273.

    Google Scholar 

  10. Blake A., Isard M., Reynard D.: learning to track the visual motion of contours. International Journal of Artificial Intelligent(78). (1995) 101–134.

    Google Scholar 

  11. Blake A., Isard M.: Active Contours. Springer-Verlag. (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lee, HS., Kim, D., Lee, SY. (2003). Robust Face-Tracking Using Skin Color and Facial Shape. In: Kittler, J., Nixon, M.S. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2003. Lecture Notes in Computer Science, vol 2688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44887-X_36

Download citation

  • DOI: https://doi.org/10.1007/3-540-44887-X_36

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40302-9

  • Online ISBN: 978-3-540-44887-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics