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Gait Identification Based on Multi-view Observations Using Omnidirectional Camera

  • Kazushige Sugiura
  • Yasushi Makihara
  • Yasushi Yagi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)

Abstract

We propose a method of gait identification based on multi-view gait images using an omnidirectional camera. We first transform omnidirectional silhouette images into panoramic ones and obtain a spatio-temporal Gait Silhouette Volume (GSV). Next, we extract frequency- domain features by Fourier analysis based on gait periods estimated by autocorrelation of the GSVs. Because the omnidirectional camera makes it possible to observe a straight-walking person from various views, multi-view features can be extracted from the GSVs composed of multi-view images. In an identification phase, distance between a probe and a gallery feature of the same view is calculated, and then these for all views are integrated for matching. Experiments of gait identification including 15 subjects from 5 views demonstrate the effectiveness of the proposed method.

Keywords

False Alarm Rate Azimuth Angle Gait Feature Gait Recognition Omnidirectional Image 
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.
    Urtasun, R., Fua, P.: 3d tracking for gait characterization and recognition. In: Proc. of the 6th IEEE Int. Conf. on Automatic Face and Gesture Recognition, pp. 17–22. IEEE Computer Society Press, Los Alamitos (2004)CrossRefGoogle Scholar
  2. 2.
    Yam, C., Nixon, M., Carter, J.: Automated person recognition by walking and running via model-based approaches. Pattern Recognition 37(5), 1057–1072 (2004)CrossRefGoogle Scholar
  3. 3.
    Sarkar, S., Phillips, J., Liu, Z., Vega, I., Grother, P., Bowyer, K.: The humanid gait challenge problem: Data sets, performance, and analysis. Trans. of Pattern Analysis and Machine Intelligence 27(2), 162–177 (2005)CrossRefGoogle Scholar
  4. 4.
    Han, J., Bhanu, B.: Individual recognition using gait energy image. Trans. on Pattern Analysis and Machine Intelligence 28(2), 316–322 (2006)CrossRefGoogle Scholar
  5. 5.
    Yu, S., Tan, D., Tan, T.: Modelling the effect of view angle variation on appearance-based gait recognition. In: Proc. of 7th Asian Conf. on Computer Vision, vol. 1, pp. 807–816 (2006)Google Scholar
  6. 6.
    Kale, A., Roy-Chowdhury, A., Chellappa, R.: Towards a view invariant gait recognition algorithm. In: Proc. of IEEE Conf. on Advanced Video and Signal Based Surveillance, pp. 143–150. IEEE Computer Society Press, Los Alamitos (2003)CrossRefGoogle Scholar
  7. 7.
    Shakhnarovich, G., Lee, L., Darrell, T.: Integrated face and gait recognition from multiple views. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 439–446 (2001)Google Scholar
  8. 8.
    Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., Yagi, Y.: Gait recognition using a view transformation model in the frequency domain. In: Proc. of the 9th European Conf. on Computer Vision, Graz, Austria, vol. 3, pp. 151–163 (2006)Google Scholar
  9. 9.
    Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., Yagi, Y.: Which reference view is effective for gait identification using a view transformation model? In: Proc. of the IEEE Computer Society Workshop on Biometrics 2006, New York, USA (2006)Google Scholar
  10. 10.
    Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., Yagi, Y.: Adaptation to walking direction changes for gait identification. In: Proc. of the 18th Int. Conf. on Pattern Recognition, Hong Kong, China, vol. 2, pp. 96–99 (2006)Google Scholar
  11. 11.
    Yamazawa, K., Yagi, Y., Yachida, M.: Hyperomni vision: Visual navigation with an omnidirectional image sensor. Systems and Computers in Japan 28(4), 36–47 (1997)CrossRefGoogle Scholar
  12. 12.
    Phillips, P., Moon, H., Rizvi, S., Rauss, P.: The feret evaluation methodology for face-recognition algorithms. Trans. of Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Kazushige Sugiura
    • 1
  • Yasushi Makihara
    • 1
  • Yasushi Yagi
    • 1
  1. 1.Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047Japan

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