Analysis of spatial domain information for footstep recognition

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Analysis of spatial domain information for footstep recognition

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© The Institution of Engineering and Technology
Published

This study reports an experimental analysis of footsteps as a biometric. The focus here is on information extracted from the spatial domain of signals collected from an array of piezoelectric sensors. Results are related to the largest footstep database collected to date, with almost 20 000 valid footstep signals and more than 120 persons. A novel feature approach is proposed, obtaining three-dimensional images of the distribution of the footstep pressure along the spatial course. Experimental work is based on a verification mode with a holistic approach based on principal component analysis and support vector machines, achieving results in the range of 6–10% equal error rate (EER) depending on the experimental conditions of quantity of data used in the client models (200 and 40 signals per model, respectively). Also, this study includes the analysis of two interesting factors affecting footstep signals and especially spatial domain features, namely, sensor density and the special case of high heels.

Inspec keywords: biometrics (access control); sensors; principal component analysis; signal processing; support vector machines

Other keywords: verification mode; equal error rate; piezoelectric sensor array; principal component analysis; holistic approach; support vector machines; high heels; sensor density; footstep recognition; spatial domain information analysis; footstep signals; footstep database

Subjects: Other topics in statistics; Other topics in statistics; Knowledge engineering techniques; Signal processing and detection; Digital signal processing

References

    1. 1)
      • Vera-Rodriguez, R., Lewis, R.P., Mason, J.S.D., Evans, N.W.D.: `A large scale footsteps database for biometric studies created using cross-biometrics for labelling', Proc. 10th IEEE Int. Conf. on Control, Automation, Robotics and Vision (ICARCV), 2008, Hanoi, Vietnam, p. 1361–1366.
    2. 2)
      • Yun, J.S., Lee, S.H., Woo, W.T., Ryu, J.H.: `The user identification system using walking pattern over the ubiFloor', Proc. Int. Conf. on Control, Automation, and Systems, 2003, p. 1046–1050.
    3. 3)
      • Vera-Rodriguez, R., Mason, J., Evans, N.: `Automatic cross-biometric footstep database labelling using speaker recognition', Proc. 3rd IAPR/IEEE Int. Conf. on Biometrics (ICB), June 2009, p. 503–512, (LNCS, 5558).
    4. 4)
    5. 5)
      • Cattin, C.: `Biometric authentication system using human gait', 2002, PhD, Swiss Federal Institute of Technology, ETH, Zurich.
    6. 6)
      • Suutala, J., Roning, J.: `Combining classifiers with different footstep feature sets and multiple samples for person identification', Proc. Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2005, 5, p. 357–360.
    7. 7)
      • Orr, R.J., Abowd, G.D.: `The smart floor: a mechanism for natural user identification and tracking', Proc. Conf. on Human Factors in Computing Systems, 2000.
    8. 8)
      • Vera-Rodriguez, R., Mason, J., Fierrez, J., Ortega-Garcia, J.: `Analysis of time domain information for footstep recognition', Proc. 6th Int. Symp. on Visual Computing, (ISVC’2010), 2010, Las Vegas, NV, USA, p. 489–498, (LNCS, 6453).
    9. 9)
      • Suutala, J., Pirttikangas, S., Riekki, J., Roning, J.: `Reject-optional LVQ-based two-level classifier to improve reliability in footstep identification', Proc. Pervasive Computing, Vienna, Austria, 2004, p. 182–187, (LNCS, 3001).
    10. 10)
      • V.N. Vapnik . (1998) Statistical learning theory.
    11. 11)
    12. 12)
      • Gao, Y., Brennan, M.J., Mace, B.R., Muggleton, J.M.: `Person recognition by measuring the ground reaction force due to a footstep', Proc. 9th Int. Conf. on Recent Advances in Structural Dynamics, 2006, Southampton, UK.
    13. 13)
      • Suutala, J., Fujinami, K., Röning, J.: `Gaussian process person identifier based on simple floor sensors', Proc. 3rd Eur. Conf. on Smart Sensing and Context, (EuroSSC’08), 2008, Zurich, Switzerland, p. 55–68.
    14. 14)
      • Middleton, L., Buss, A.A., Bazin, A.I., Nixon, M.S.: `A floor sensor system for gait recognition', Proc. Fourth IEEE Workshop on Automatic Identification Advanced Technologies, (AutoID’05), 2005, p. 171–176.
    15. 15)
      • R. Vera-Rodriguez , R. Lewis , J. Mason , N. Evans . Footstep recognition for a smart home environment. Int. J. Smart Home , 95 - 110
    16. 16)
      • I. Joliffe . (1986) Principal component analysis.
    17. 17)
      • R. Vera-Rodriguez , N. Evans , J. Mason . (2009) Footstep recognition, Encyclopaedia of biometrics.
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