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Partitioning Gait Cycles Adaptive to Fluctuating Periods and Bad Silhouettes

  • Jianyi Liu
  • Nanning Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)

Abstract

Period detection and cycle partitioning are always the very beginning for most gait recognition algorithms. Badly segmented silhouettes and random fluctuations in walking speed are two of the main problems for this basic but important issue. In this paper, we propose a method of cycle partitioning that is adaptive to silhouette quality and speed fluctuations. To do that, autocorrelation on sliding window is proposed to quantify the silhouette quality into “trusted zones” and “uncertain zones”. Prior period estimation and observation of fluctuations are incorporated to obtain more precise cycle detection. One criterion based on the difference of Common Phase Frames (CPF) is proposed to evaluate the precision of detection. In experiment, our method was compared with the traditional autocorrelation method using sequences from the USF gait database. The results showed the improved cycle partitioning performance of the proposed method.

Keywords

Gait Cycle Gesture Recognition Dynamic Time Warping Gait Recognition Automatic Face 
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.
    Boulgouris, N.V., Hatzinakos, D., Plataniotis, K.N.: Gait recognition: a challenging signal processing technology for biometric identification. IEEE Signal Processing Magazine 22(6), 78–90 (2005)CrossRefGoogle Scholar
  2. 2.
    Tanawongsuwan, R., Bobick, A.: Modelling the Effects of Walking Speed on Appearance-Based Gait Recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 783–790 (2004)Google Scholar
  3. 3.
    Little, J., Boyd, J.: Recognizing people by their gait: The shape of motion. Videre, Int. J. Computer Vision 14(6), 83–105 (1998)Google Scholar
  4. 4.
    Sundaresan, A., Roy Chowdhury, A.K., Chellappa, R.: A hidden Markov model based framework for recognition of humans from gait sequences. In: Proc. Int. Conf. Image Processing, vol. 2, pp. 14–17 (2003)Google Scholar
  5. 5.
    Boulgouris, N.V., Plataniotis, K.N., Hatzinakos, D.: Gait recognition using dynamic time warping. In: Proc. IEEE Int. Symp. Multimedia Signal Processing, pp. 263–266 (September 2004)Google Scholar
  6. 6.
    BenAbdelkader, C., Cutler, R., Davis, L.: Stride and cadence as a biometric in automatic person identification and verification. In: 5th International Conference on Automatic Face and Gesture Recognition (2002)Google Scholar
  7. 7.
    Chai, Y., Ren, J., Zhao, R., Jia, J.: Automatic Gait Recognition using Dynamic Variance Features. In: 7th International Conference on Automatic Face and Gesture Recognition, pp. 475–480 (2006)Google Scholar
  8. 8.
    Lee, L., Dalley, G., Tieu, K.: Learning pedestrian models for silhouette refinement. In: Proc. of Ninth IEEE International Conference on Computer Vision, pp. 663–670 (2003)Google Scholar
  9. 9.
    Han, J., Bhanu, B.: Individual Recognition Using Gait Energy Image. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(2), 316–322 (2006)CrossRefGoogle Scholar
  10. 10.
    Sarkar, S., Phillips, P.J., Liu, Z., Vega, I.R., Grother, P., Bowyer, K.W.: The Humanid Gait Challenge Problem: Data Sets, Performance, and Analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(2), 162–177 (2005)CrossRefGoogle Scholar
  11. 11.
    Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: A Layered Deformable Model for Gait Analysis. In: Proc. IEEE Int. Conf. on Automatic Face and Gesture Recognition, pp. 249–256 (2006)Google Scholar
  12. 12.
    Wang, L., Tan, T., Ning, H., Hu, W.: Fusion of Static and Dynamic Body Biometrics for Gait Recognition. IEEE Trans. on Circuits and Systems for Video Technology 14(I2), 149–158 (2004)CrossRefGoogle Scholar
  13. 13.
    Wagg, D.K., Nixon, M.S.: On Automated Model-based Extraction and Analysis of Gait. In: 6th International Conference on Automatic Face and Gesture Recognition, pp. 11–16 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Jianyi Liu
    • 1
  • Nanning Zheng
    • 1
  1. 1.Institute of AI & Robotics, Xi’an Jiaotong UniversityP.R. China

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