Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

A Convolutional Neural Network for Gait Recognition Based on Plantar Pressure Images

  • Conference paper
  • First Online:
Biometric Recognition (CCBR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10568))

Included in the following conference series:

Abstract

This paper proposed a novel gait recognition method that is based on plantar pressure images. Different from many conventional methods where hand-crafted features are extracted explicitly. We utilized Convolution Neural Network (CNN) for automatic feature extraction as well as classification. The peak pressure image (PPI) generated from the time series of plantar pressure images is used as the characteristic image for gait recognition in this study. Our gait samples are collected from 109 subjects under three kinds of walking speeds, and for each subject total 18 samples are gathered. Experimental results demonstrate that the designed CNN model can obtain very high classification accuracy as compared to many traditional methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ben, X., Xu, S., Wang, K.: Review on pedestrian gait feature expression and recognition. Pattern Recognit. Artif. Intell. 25, 71–81 (2012)

    Google Scholar 

  2. Liu, J., Zheng, N.: Gait history image: a novel temporal template for gait recognition. In: 2007 IEEE International Conference on Multimedia and Expo, pp. 663–666. IEEE (2007)

    Google Scholar 

  3. Yang, J., Wu, X., Peng, Z.: Gait recognition based on difference motion slice. In: 2006 8th International Conference on Signal Processing. IEEE (2006)

    Google Scholar 

  4. Moustakidis, S.P., Theocharis, J.B., Giakas, G.: Subject recognition based on ground reaction force measurements of gait signals. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 38, 1476–1485 (2008)

    Article  Google Scholar 

  5. Vera-Rodriguez, R., Mason, J.S., Fierrez, J., Ortega-Garcia, J.: Comparative analysis and fusion of spatiotemporal information for footstep recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35, 823–834 (2013)

    Article  Google Scholar 

  6. Jung, J.-W., Bien, Z., Lee, S.-W., Sato, T.: Dynamic-footprint based person identification using mat-type pressure sensor. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2937–2940. IEEE (2003)

    Google Scholar 

  7. Pataky, T.C., Mu, T., Bosch, K., Rosenbaum, D., Goulermas, J.Y.: Gait recognition: highly unique dynamic plantar pressure patterns among 104 individuals. J. R. Soc. Interface 9, 790–800 (2012)

    Article  Google Scholar 

  8. Pataky, T.C., Goulermas, J.Y., Crompton, R.H.: A comparison of seven methods of within-subjects rigid-body pedobarographic image registration. J. Biomech. 41, 3085–3089 (2008)

    Article  Google Scholar 

  9. Jia, W., Hu, R.-X., Gui, J., Lei, Y.-K.: Newborn footprint recognition using band-limited phase-only correlation. In: Zhang, D., Sonka, M. (eds.) ICMB 2010. LNCS, vol. 6165, pp. 83–93. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13923-9_9

    Chapter  Google Scholar 

  10. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  11. Bazzani, L., Cristani, M., Murino, V.: Symmetry-driven accumulation of local features for human characterization and re-identification. Comput. Vis. Image Underst. 117, 130–144 (2013)

    Article  MATH  Google Scholar 

  12. Acharya, Y.R.: Mapping layer 2 LAN priorities to a virtual lane in an Infinibandâ„¢ network. Google Patents (2006)

    Google Scholar 

  13. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)

  14. Vedaldi, A., Lenc, K.: MatConvNet: convolutional neural networks for MATLAB. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 689–692. ACM (2015)

    Google Scholar 

  15. Xia, Y., Ma, Z., Yao, Z., Sun, Y.: Gait recognition based on spatio-temporal HOG of plantar pressure distribution. Pattern Recognit. Artif. Intell. 26, 529–536 (2013)

    Google Scholar 

  16. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2, 27 (2011)

    Google Scholar 

Download references

Acknowledgments

This work is supported by Anhui Provincial Natural Science Foundation (grant number 1608085MF136); China Postdoctoral Science Foundation (2015M582826); Major University Science Research Project of Anhui Province (grant number KJ2016SD33); Anhui Province Science and Technology Major Project (grant number 1603081122); National Natural Science Foundation of China (NSFC) for Youth (grant numbers 61402004, 61602002).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yi Xia .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Li, Y. et al. (2017). A Convolutional Neural Network for Gait Recognition Based on Plantar Pressure Images. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-69923-3_50

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69922-6

  • Online ISBN: 978-3-319-69923-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics