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A Multi-view Method for Gait Recognition Using Static Body Parameters

  • Amos Y. Johnson
  • Aaron F. Bobick
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2091)

Abstract

A multi-view gait recognition method using recovered static body parameters of subjects is presented; we refer to these parameters as activity-specific biometrics. Our data consists of 18 subjects walking at both an angled and frontal-parallel view with respect to the camera. When only considering data from a single view, subjects are easily discriminated; however, discrimination decreases when data across views are considered. To compare between views, we use ground truth motioncapture data of a reference subject to find scale factors that can transform data from different viewsi nto a common frame (“walking-space”). Instead of reporting percent correct from a limited database, we report our results using an expected confusion metric that allows us to predict how our static body parameters filter identity in a large population: lower confusion yields higher expected discrimination power. We show that using motion-capture data to adjust vision data of different views to a common reference frame, we can get achieve expected confusions rates on the order of 6%.

Keywords

Mutual Information Vision Data View Angle Gait Recognition Reference Subject 
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.
    Bobick, A.F. and A.Y. Johnson, “Expected Confusion as a Method of Evaluating Recognition Techniques,” Technical Report GIT.GVU-01-01, Georgia Institute of Technology, 2001. http://www.gvu.gatech.edu/reports/2001/.
  2. 2.
    Cover, T.M. and J.A. Thomas, Elements of Information Theory, John Wilety & Sons, Inc., New York, 1991.zbMATHGoogle Scholar
  3. 3.
    Cunado, D., M.S. Nixion, and J.N. Carter, “Automatic Gait Recognition via Model-Based Evidence Gathering,” accepted for IEEE AutoID99, Summit NJ, 1999.Google Scholar
  4. 4.
    Cutting, J. and L. Kozlowski, “Recognizing friends by their walk: Gait perception without familiarity cues,” Bulletin of the Psychonomic Society 9 pp. 353–356, 1977.Google Scholar
  5. 5.
    Davis, J.W. and A.F. Bobick, “The representation and recognition of action using temporal templates,” Proc. IEEE Computer Vision and Pattern Recognition, San Juan, Puerto Rico, pp 928–934, 1997.Google Scholar
  6. 6.
    Haritaoglu, I., D. Harwood, and L. Davis, “W4: Who, When, Where, What: A real time system for detecting and tracking people,” Proc. of Third Face and Gesture Recognition Conference, pp. 222–227, April 1998.Google Scholar
  7. 7.
    Huang, P.S., C.J. Harris, and M.S. Nixon, “Human Gait Recognition in Canonical Space using Temporal Templates,” IEEE Procs. Vision Image and Signal Processing, 146(2), pp. 93–100, 1999.CrossRefGoogle Scholar
  8. 8.
    Kozlowski, L. and J. Cutting, “Recognizing the sex of a walker from a dynamic point-light display,” Perception and Psychophysics, 21 pp. 575–580, 1977.Google Scholar
  9. 9.
    Little, J.J. and J.E. Boyd, “Recognizing people by their gait: the shape of motion,” Videre, 1, 1996.Google Scholar
  10. 10.
    Murase, H. and R. Sakai, “Moving object recognition in eigenspace representation: gait analysis and lip reading,” Pattern Recognition Letters, 17, pp. 155–162, 1996.CrossRefGoogle Scholar
  11. 11.
    Niyogi, S. and E. Adelson, “Analyzing and Recognizing Walking Figures in XYT,” Proc. Computer Vision and Pattern Recognition, pp. 469–474, Seattle, 1994.Google Scholar
  12. 12.
    Stevenage, S., M.S. Nixon, and K. Vince, “Visual Analysis of Gait as a Cue to Identity,” Applied Cognitive Psychology, 13, pp. 00–00, 1999.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Amos Y. Johnson
    • 1
  • Aaron F. Bobick
    • 2
  1. 1.Electrical and Computer EngineeringGeorgia TechAtlanta
  2. 2.GVU Center/College of ComputingGeorgia TechAtlanta

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