Abstract
Gait as a motion-based biometric has the merit of being non-contact and unobtrusive. In this paper, we proposed a gait recognition approach using spectral features of horizontal and vertical movement of ankles in a normal walk. Gait recognition experiments using the spectral features in term of the magnitude, phase and phase-weighted magnitude show that both magnitude and phase spectra are effective gait signatures, but magnitude spectra are slightly superior. We also proposed the use of geometrical mean based spectral features for gait recognition. Experimental results with 9 subjects show encouraging results in the same-day test, while the effect of time covariate is confirmed in the cross-month test.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Johansson, G.: Visual Motion Perception. Scientific American, 75–80, 85–88 (June 1975)
Cutting, J., Kozlowski, L.: Recognizing Friends by Their Walk: Gait perception Without Familiarity Cues. Bulletin Psychonomic Society 9(5), 353–356 (1977)
Niyogi, S.A., Adelson, E.H.: Analyzing and recognizing walking figures in XYT. In: Proc. Conf. Computer vision and Pattern Recognition 1994, pp. 467–474 (1994)
Lakany, H.M., Hayes, G.M.: An Algorithm for Recognising Walkers. In: Proc. of the 1st Int’l Conference on Audio- and Video-based Person Authentication (March 1997)
Murase, H., Sakai, R.: Moving Object Recognition in Eigenspace Representation: Gait Analysis and Lip Reading. Pattern Recognition Letters 17(2), 155–162 (1997)
Cunado, D., Nixon, M.S., Carter, J.N.: Using Gait as a Biometric, via Phase-Weighted Magnitude Spectra. In: Proceedings of 1st Int. Conf. on Audio- and Video-Based Biometric Person Authentication, March 1997, pp. 95–102 (1997)
He, Q., Debruner, C.: Individual Recognition fromPeriodic Activity Using HiddenMarkov Models. In: Proc. IEEE Workshop on Human Motion (2000)
BenAbdelkader, C., Cutler, R., Davis, L.: Motion-based Recognition of People in Eigen- Gait Space. In: Proc. of the 5th IEEE Int’l Conference on Automatic Face and Gesture Recognition, May 2002, pp. 267–272 (2002)
Lee, L., Grimson, W.E.L.: Gait Appearance for Recognition. In: ECCV Workshop on Biometric Authentication (June 2002)
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)
Mowbray, S.D., Nixon, M.S.: Automatic Gait Recognition via Fourier Descriptors of Deformable Objects. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 566–573. Springer, Heidelberg (2003)
Yam, C., Nixon, M.S., Carter, J.N.: Automated Person Recognition by Walking and Running viaModel-Based Approaches. Pattern Recognition 37(5), 1057–1072 (2004)
Li, B., Holstein, H.: Perception of Human Periodic Motion in Moving Light Displays - a Motion-Based Frequency Domain Approach. Interdisciplinary Journal of Artificial Intelligence and the Simulation of Behaviour (AISBJ) 1(5), 403–416 (2004)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lie, A.S. et al. (2005). Gait Recognition Using Spectral Features of Foot Motion. In: Kanade, T., Jain, A., Ratha, N.K. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2005. Lecture Notes in Computer Science, vol 3546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527923_80
Download citation
DOI: https://doi.org/10.1007/11527923_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27887-0
Online ISBN: 978-3-540-31638-1
eBook Packages: Computer ScienceComputer Science (R0)