Reconstruction of 3D Human Body Pose for Gait Recognition

  • Hee-Deok Yang
  • Seong-Whan Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


In this paper, we propose a novel method to reconstruct 3D human body pose for gait recognition from monocular image sequences based on top-down learning. Human body pose is represented by a linear combination of prototypes of 2D silhouette images and their corresponding 3D body models in terms of the position of a predetermined set of joints. With a 2D silhouette image, we can estimate optimal coefficients for a linear combination of prototypes of the 2D silhouette images by solving least square minimization. The 3D body model of the input silhouette image is obtained by applying the estimated coefficients to the corresponding 3D body model of prototypes. In the learning stage, the proposed method is hierarchically constructed by classifying the training data into several clusters recursively. Also, in the reconstructing stage, the proposed method hierarchically reconstructs 3D human body pose with a silhouette image. The experimental results show that our method can be efficient and effective to reconstruct 3D human body pose for gait recognition.


Gait Recognition Silhouette Image Gait Representation Monocular Image Sequence Gesture Database 
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.


  1. 1.
    Benadbdlkader, et al.: Gait Recognition using Image Self-Similarity. EURASIO Journal on Applied Signal Processing 4, 1–14 (2004)Google Scholar
  2. 2.
    Bissacoo, A., et al.: Recognition of Human Gaits. In: Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Hawaii, USA, December 2001, pp. 52–57 (2001)Google Scholar
  3. 3.
    Bowden, R., Mitchell, T.A., Sarhadi, M.: Non-linear Statistical Models for 3D Reconstruction of Human Pose and Motion from Monocular Image Sequences. Image and Vision Computing 18(9), 729–737 (2000)CrossRefGoogle Scholar
  4. 4.
    Collins, R., Gross, R., Shi, J.: Silhouette-based Human Identification from Body Shape and Gait. In: Proc. of the IEEE International Conference on Automatic Face and Gesture Recognition, Washington D.C., USA, May 2002, pp. 351–356 (2002)Google Scholar
  5. 5.
    Gross, R., Shi, J.: The CMU Motion of Body(MOBO) Database. Technical report CMU-RI-TR-01-18, Robotics Institute, Carnegie Mellon University (June 2001)Google Scholar
  6. 6.
    He, Q., Debrunner, C.: Individual Recognition from Periodic Activity using Hidden Markov Models. In: Proc. of the IEEE Workshop on Human Motion, Austin Texas, USA, December 2000, pp. 475–527 (2000)Google Scholar
  7. 7.
    Heap, T., Hogg, D.: Improving Specificity in PDMs using a Hierarchical Approach. In: Proc. of 8th British Machine Vision Conference, Colchester, UK, September 1997, pp. 590–599 (1997)Google Scholar
  8. 8.
    Hwang, B.-W., Kim, S., Lee, S.-W.: Full-Body Gesture Database for Analyzing Daily Human Gestures. In: Proc. of 1st Int’l Conf. on Intelligent Computing, Hefei, China,, August 2005, pp. 611–620 (2005)Google Scholar
  9. 9.
    Kale, A., et al.: Identification of Humans using Gait. IEEE Trans. on Image Processing 13, 1163–1173 (2004)CrossRefGoogle Scholar
  10. 10.
    Little, J., Boyd, J.: Recognizing People by Their Gait: the Shape of Motion. Videre 1(2), 1–32 (1998)Google Scholar
  11. 11.
    Murray, M.: Gait as a Total Pattern of Movement. American Journal of Physical Medicine 46(1), 290–332 (1967)Google Scholar
  12. 12.
    Rosales, R., Sclaroff, S.: Specialized Mapping and the Estimation of Human Body Pose from a Single Image. In: Proc. of IEEE Workshop on Human Motion, Texas, USA, December 2000, pp. 19–24 (2000)Google Scholar
  13. 13.
    Yam, C., et al.: Automated Person Recognition by Walking and Running via Model-based Approaches. Pattern Recognition 37(5), 1057–1072 (2004)CrossRefGoogle Scholar
  14. 14.
    The Gait Recognition Database at Georgia Tech.,
  15. 15.
    The KU Gesture Database,

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hee-Deok Yang
    • 1
  • Seong-Whan Lee
    • 1
  1. 1.Department of Computer Science and EngineeringKorea UniversitySeoulKorea

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