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Cyclostationary Processes on Shape Spaces for Gait-Based Recognition

  • David Kaziska
  • Anuj Srivastava
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3952)

Abstract

We present a novel approach to gait recognition that considers gait sequences as cyclostationary processes on a shape space of simple closed curves. Consequently, gait analysis reduces to quantifying differences between statistics underlying these stochastic processes. The main steps in the proposed approach are: (i) off-line extraction of human silhouettes from IR video data, (ii) use of piecewise-geodesic paths, connecting the observed shapes, to smoothly interpolate between them, (iii) computation of an average gait cycle within class (i.e. associated with a person) using average shapes, (iv) registration of average cycles using linear and nonlinear time scaling, (iv) comparisons of average cycles using geodesic lengths between the corresponding registered shapes. We illustrate this approach on infrared video clips involving 26 subjects.

Keywords

Gait Analysis Gait Cycle Dynamic Time Warping Shape Space Geodesic Path 
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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References

  1. 1.
    Bhanu, B., Huan, J.: Individual recognition by kinematic-based gait analysis. In: Proceedings of the 16th International Conference on Pattern Recognition, vol. 3, pp. 30343–30346 (2002)Google Scholar
  2. 2.
    Cunado, D., Nixon, M.S., Carter, J.N.: Automatic extraction and description of human gait models for recognition purposes. Computer Vision and Image Understanding 90(1), 1–41 (2003)CrossRefGoogle Scholar
  3. 3.
    Dryden, I.L., Mardia, K.V.: Statistical Shape Analysis. John Wiley & Son, Chichester (1998)MATHGoogle Scholar
  4. 4.
    Kale, A., Sundaresan, A., Rajagopalan, A.N., Cuntoor, N.P., Roy-Chowdhury, A.K., Kruger, V., Chellappa, R.: Identification of humans using gait. IEEE Transactions on Image Processing 13(9), 1163–1173Google Scholar
  5. 5.
    Klassen, E., Srivastava, A., Mio, W., Joshi, S.: Analysis of planar shapes using geodesic paths on shape spaces. IEEE Patt. Analysis and Machine Intell. 26(3), 372–383 (2004)CrossRefGoogle Scholar
  6. 6.
    Liu, Z., Sarkar, S.: Simplest representation yet for gait recognition: Averaged silhouette. In: Proc. of IEEE International Conference on Pattern Recogntion (2004)Google Scholar
  7. 7.
    Mio, W., Srivastava, A.: Elastic string models for representation and analysis of planar shapes. In: Proc. of IEEE Computer Vision and Pattern Recognition (2004)Google Scholar
  8. 8.
    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. Pattern Analysis and Machine Intelligence 27(2), 162–177 (2005)CrossRefGoogle Scholar
  9. 9.
    Veeraghavan, A., Roy-Chowdhury, A., Chellappa, R.: Role of shape and kinematics in human movement analysis. In: Processings of CVPR, vol. 01, pp. 730–737 (2004)Google Scholar
  10. 10.
    Vega, I.R., Sarkar, S.: Statistical motion model based on the change of feature relationships: human gait-based recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 25(10), 1323–1328 (2003)CrossRefGoogle Scholar
  11. 11.
    Wang, L., Tan, T., Ning, H., Hu, W.: Silhouette analysis-based gait recognition for human identification. IEEE Trans. Pattern Analysis and Machine Intelligence 25(12), 1505–1518 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • David Kaziska
    • 1
  • Anuj Srivastava
    • 2
  1. 1.Department of Mathematics and StatisticsAir Force Institute of TechnologyDaytonUSA
  2. 2.Department of StatisticsFlorida State UniversityTallahasseeUSA

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