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Background Robust Face Tracking Using Active Contour Technique Combined Active Appearance Model

  • Jaewon Sung
  • Daijin Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

This paper proposes a two stage AAM fitting algorithm that is robust to the cluttered background and a large motion. The proposed AAM fitting algorithm consists of two alternative procedures: the active contour fitting to find the contour sample that best fits the face image and then the active appearance model fitting over the best selected contour. Experimental results show that the proposed active contour based AAM provides better accuracy and convergence characteristics in terms of RMS error and convergence rate, respectively, than the existing robust AAM.

Keywords

Face Image Active Contour Active Appearance Model Warping Parameter Appearance Parameter 
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.

References

  1. 1.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. IEEE Trans. Pattern Analysis and Machine Intelligence 23(6), 681–685 (2001)CrossRefGoogle Scholar
  2. 2.
    Edwards, G.J., Taylor, C.J., Cootes, T.F.: Interpreting Face Images Using Active Appearance Models. In: Proc. of IEEE 3rd International Conference on Automatic Face and Gesture Recognition, vol. 0, p. 300 (1998)Google Scholar
  3. 3.
    Edwards, G.J., Cootes, T.F., Taylor, C.J.: Face Recognition Using Active Appearance Models. In: Proc. of 5th European Conference on Computer Vision, vol. 2, p. 581 (1998)Google Scholar
  4. 4.
    Baker, S., Matthews, I.: Active Appearance Models Revisited, CMU-RI-TR-03-01, CMU (April 2003)Google Scholar
  5. 5.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proc. of International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)Google Scholar
  6. 6.
    Matthews, I., Gross, R., Baker, S.: Lucas-Kanade 20 Years on: A Unifying Framework: Part 3, CMU-RI-TR-03-05, CMU (November 2003)Google Scholar
  7. 7.
    Isard, M., Blake, A.: CONDENSATION-Conditional Density Propagation for Visual Tracking. International Journal of Computer Vision 29, 5–28 (1998)CrossRefGoogle Scholar
  8. 8.
    Isard, M., Blake, A.: Active Contours. Springer, Heidelberg (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jaewon Sung
    • 1
  • Daijin Kim
    • 1
  1. 1.Biometrics Engineering Research Center (BERC)Pohang University of Science and Technology 

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