Robust Face-Tracking Using Skin Color and Facial Shape

  • Hyung-Soo Lee
  • Daijin Kim
  • Sang-Youn Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2688)


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.


Skin Color Color Histogram Facial Shape Clutter Background Face Tracking 
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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  1. [1]
    Yang, J., Waibel A.: A Real-Time Face Tracker. Proceeding of WACV.(1996) 142–147.Google Scholar
  2. [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. [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. [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. [5]
    Birchfield S.: An Elliptical Head Tracker. 31st Asilomar Conference. (1997) 1710–1714.Google Scholar
  6. [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. [7]
    Isard M., Blake A.: CONDENSATION-conditional density propagation for visual tracking. International Journal of Computer Vision 29(1). (1998) 5–28.CrossRefGoogle Scholar
  8. [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. [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. [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. [11]
    Blake A., Isard M.: Active Contours. Springer-Verlag. (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Hyung-Soo Lee
    • 1
  • Daijin Kim
    • 2
  • Sang-Youn Lee
    • 3
  1. 1.Department of Computer Science and EngineeringPohang University of Science and TechnologyKorea
  2. 2.Department of Computer Science and EngineeringPohang University of Science and TechnologyKorea
  3. 3.Multimedia Technology LaboratoryKorea TelecomSeoulKorea

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