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Using gait as a biometric, via phase-weighted magnitude spectra

  • Visual Non-face Biometrics
  • Conference paper
  • First Online:
Audio- and Video-based Biometric Person Authentication (AVBPA 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1206))

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Abstract

Gait is a biometric which is subject to increasing interest. Current approaches include modelling gait as a spatio-temporal sequence and as an articulated model. By considering legs only, gait can be considered to be the motion of interlinked pendula. We describe how the Hough transform is used to extract the lines which represent legs in sequences of video images. The change in inclination of these lines follows simple harmonic motion; this motion is used as the gait biometric. The method of least squares is used to smooth the data and to infill for missing points. Then, Fourier transform analysis is used to reveal the frequency components of the change in inclination of the legs. The transform data is then classified using the k-nearest neighbour rule. Experimental analysis shows how phase-weighted Fourier magnitude spectra afford an improved classification rate over use of just magnitude spectra. Accordingly, it appears that it is not just the frequency content which makes gait a practical biometric, but its phase as well.

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References

  1. J.Z. Achimowicz, “Variability analysis of visual evoked potentials in humans by pattern recognition in phase domain”, Acta Neurobiologiae Experiments, 55(3), pp.177–191, 1995.

    Google Scholar 

  2. C. Angeloni, P.O. Riley, D.E. Krebs, “Frequency content of whole body gait kinematic data”, IEEE Trans. Rehab. Eng., 2(1), pp.40–46, 1994.

    Google Scholar 

  3. A. Azarbayejani, C. Wren and A. Pentland, “Real-Time 3-D Tracking of the Human Body”, Proc. IMAGE'COM, May 1996.

    Google Scholar 

  4. L. Campbell and A. Bobick, “Recognition of Human Body Motion using Phase Space Constraints”, MIT Media Lab Perceptal Computing Report 309, 1995.

    Google Scholar 

  5. Q. Chen, M. Defrise and F. Decconinck, “Symmetric phase-only matched filtering of Fourier-Mellin Transforms for image registration and recognition”, IEEE Trans. PAMI, 16(12), pp.1156–1168, 1994.

    Google Scholar 

  6. G. Gerig and F. Klein, “Fast contour identification through efficient Hough Transform and simplified interpretation strategy”, Proc. 8th ICPR, pp.498–500, 1986.

    Google Scholar 

  7. D. Hogg, “Model-based vision — a program to see a walking person”, Image and Vision Computing, 1(1), pp.5–20, 1983.

    Google Scholar 

  8. E.L. Kuan, “Investigating gait as a biometric”, Report, Department of Electronics and Computer Science, University of Southampton, 1995.

    Google Scholar 

  9. H. Murase and R. Sakai, “Moving object recognition in eigenspace representation: gait analysis and lip reading”, Pattern Recognition Letters, 17, pp155–162, 1996.

    Google Scholar 

  10. M.P. Murray, “Gait as a total pattern of movement”, American Journal of Physical Medicine, 46(1), pp.290–332, 1967.

    Google Scholar 

  11. M.P. Murray, A.B. Drought, R.C. Kory, “Walking patterns of normal men”, Journal of Bone and Joint Surgery, 46-A(2), pp.335–360, 1964.

    Google Scholar 

  12. A.K. Nandi and E.E. Azzouz, “Automatic analogue modulation recognition”, Signal Processing, 46, pp.211–222, 1995.

    Google Scholar 

  13. S.A. Niyogi, E.H. Adelson, “Analyzing and recognizing walking figures in XYT”, Proc. Conf. Comp. Vis. and Pattern Recog. 1994, pp.469–474, 1994.

    Google Scholar 

  14. K. Rohr, “Towards model-based recognition of human movements in image sequences”, CVGIP, 59(1), pp.94–115, 1994.

    Google Scholar 

  15. S.S. Soliman and S. Hsue, “Signal classification using statistical moments”, IEEE Trans. Comms., 40(5), pp.908–916, 1992.

    Google Scholar 

  16. Y. Yang and S.S. Soliman, “An improved moment-based algorithm for signal classification”, Signal Processing, 43, pp.231–244, 1995.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronics and Computer Science, University of Southampton, SO17 1BJ, Highfield, Southampton, England

    David Cunado, Mark S. Nixon & John N. Carter

Authors
  1. David Cunado
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  2. Mark S. Nixon
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  3. John N. Carter
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Editor information

Josef Bigün Gérard Chollet Gunilla Borgefors

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© 1997 Springer-Verlag Berlin Heidelberg

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Cunado, D., Nixon, M.S., Carter, J.N. (1997). Using gait as a biometric, via phase-weighted magnitude spectra. In: Bigün, J., Chollet, G., Borgefors, G. (eds) Audio- and Video-based Biometric Person Authentication. AVBPA 1997. Lecture Notes in Computer Science, vol 1206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0015984

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  • DOI: https://doi.org/10.1007/BFb0015984

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62660-2

  • Online ISBN: 978-3-540-68425-1

  • eBook Packages: Springer Book Archive

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