Skip to main content
SpringerLink
Log in
Book cover

Asian Conference on Computer Vision

ACCV 2016: Computer Vision – ACCV 2016 pp 52–67Cite as

Speed Invariance vs. Stability: Cross-Speed Gait Recognition Using Single-Support Gait Energy Image

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10112))

Abstract

Gait recognition has recently attracted much attention since it can identify person at a distance without subject cooperation. Walking speed changes, however, cause gait changes in appearance, which significantly drops performance of gait recognition. Considering a speed-invariant property at single-support phases where stride change due to speed changes are mitigated, and a stability against phase estimation error and segmentation noise by aggregating multiple phases inspired by gait energy image (GEI), we propose a speed-invariant gait representation called single-support GEI (SSGEI), which realizes a good trade-off between the speed invariance and the stability by combining single-support phases and GEI concept. For this purpose, we firstly find out the optimal duration around single support phases using a training set so as to well balance the speed invariance and the stability. We then extract SSGEI by aggregating multiple single-support frames. Finally, we combine the proposed SSGEI with subsequent Gabor filters and metric learning for better performance. Experiments on the publicly available OU-ISIR Treadmill Dataset A composed of the largest speed variations demonstrated that the proposed method yielded 99.33% rank-1 identification rate on average for cross-speed gait recognition, which outperforms the other state-of-the-arts, and realized a low computational cost as well.

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

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

Learn about institutional subscriptions

Notes

  1. 1.

    The vertical positions of the foot bottom and the head top are represented as 0 and H, respectively, in this coordinate system.

References

  1. Nixon, M.S., Tan, T.N., Chellappa, R.: Human Identification Based on Gait. International Series on Biometrics. Springer, New York (2005)

    Google Scholar 

  2. Bouchrika, I., Goffredo, M., Carter, J., Nixon, M.: On using gait in forensic biometrics. J. Forensic Sci. 56, 882–889 (2011)

    Article  Google Scholar 

  3. Iwama, H., Muramatsu, D., Makihara, Y., Yagi, Y.: Gait verification system for criminal investigation. IPSJ Trans. Comput. Vis. Appl. 5, 163–175 (2013)

    Article  Google Scholar 

  4. Lynnerup, N., Larsen, P.: Gait as evidence. IET Biom. 3, 47–54 (2014)

    Article  Google Scholar 

  5. Sarkar, S., Phillips, J., Liu, Z., Vega, I., Grother, P., Bowyer, K.: The humanid gait challenge problem: data, sets, performance and analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27, 162–177 (2005)

    Article  Google Scholar 

  6. Bouchrika, I., Nixon, M.: Exploratory factor analysis of gait recognition. In: Proceedings of 8th IEEE International Conference on Automatic Face and Gesture Recognition, Amsterdam, The Netherlands, pp. 1–6 (2008)

    Google Scholar 

  7. Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28, 316–322 (2006)

    Article  Google Scholar 

  8. Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., Yagi, Y.: Gait recognition using a view transformation model in the frequency domain. In: Proceedings of 9th European Conference on Computer Vision, Graz, Austria, pp. 151–163 (2006)

    Google Scholar 

  9. Wang, C., Zhang, J., Wang, L., Pu, J., Yuan, X.: Human identification using temporal information preserving gait template. IEEE Trans. Pattern Anal. Mach. Intell. 34, 2164–2176 (2012)

    Article  Google Scholar 

  10. Lam, T.H.W., Cheung, K.H., Liu, J.N.K.: Gait flow image: a silhouette-based gait representation for human identification. Pattern Recogn. 44, 973–987 (2011)

    Article  MATH  Google Scholar 

  11. Boulgouris, N., Plataniotis, K., Hatzinakos, D.: Gait recognition using dynamic time warping. In: Proceedings of IEEE 6th Workshop on Multimedia Signal Processing, pp. 263–266 (2004)

    Google Scholar 

  12. Veeraraghavan, A., Roy-Chowdhury, A.K., Chellappa, R.: Matching shape sequences in video with applications in human movement analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1896–1909 (2005)

    Article  Google Scholar 

  13. Boulgouris, N., Plataniotis, K., Hatzinakos, D.: Gait recognition using linear time normalization. Pattern Recogn. 39, 969–979 (2006)

    Article  MATH  Google Scholar 

  14. Veeraraghavan, A., Srivastava, A., Roy-Chowdhury, A.K., Chellappa, R.: Rate-invariant recognition of humans and their activities. IEEE Trans. Image Process. 18, 1326–1339 (2009)

    Article  MathSciNet  Google Scholar 

  15. Makihara, Y., Tsuji, A., Yagi, Y.: Silhouette transformation based on walking speed for gait identification. In: Proceedings of 23rd IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA (2010)

    Google Scholar 

  16. Tanawongsuwan, R., Bobick, A.: Modelling the effects of walking speed on appearance-based gait recognition. In: Proceedings of 17th IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 783–790 (2004)

    Google Scholar 

  17. Liu, Z., Sarkar, S.: Improved gait recognition by gait dynamics normalization. IEEE Trans. Pattern Anal. Mach. Intell. 28, 863–876 (2006)

    Article  Google Scholar 

  18. Kusakunniran, W., Wu, Q., Zhang, J., Li, H.: Speed-invariant gait recognition based on procrustes shape analysis using higher-order shape configuration. In: 18th IEEE International Conference on Image Processing, pp. 545–548 (2011)

    Google Scholar 

  19. Kusakunniran, W., Wu, Q., Zhang, J., Li, H.: Gait recognition across various walking speeds using higher order shape configuration based on a differential composition model. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 42, 1654–1668 (2012)

    Article  Google Scholar 

  20. Guan, Y., Li, C.T.: A robust speed-invariant gait recognition system for walker and runner identification. In: Proceedings of 6th IAPR International Conference on Biometrics, pp. 1–8 (2013)

    Google Scholar 

  21. Makihara, Y., Yagi, Y.: Silhouette extraction based on iterative spatio-temporal local color transformation and graph-cut segmentation. In: Proceedings of 19th International Conference on Pattern Recognition, Tampa, Florida, USA (2008)

    Google Scholar 

  22. Hossain, M.A., Makihara, Y., Wang, J., Yagi, Y.: Clothing-invariant gait identification using part-based clothing categorization and adaptive weight control. Pattern Recogn. 43, 2281–2291 (2010)

    Article  Google Scholar 

  23. Tao, D., Li, X., Wu, X., Maybank, S.J.: General tensor discriminant analysis and gabor features for gait recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29, 1700–1715 (2007)

    Article  Google Scholar 

  24. Xu, D., Huang, Y., Zeng, Z., Xu, X.: Human gait recognition using patch distribution feature and locality-constrained group sparse representation. IEEE Trans. Image Process. 21, 316–326 (2012)

    Article  MathSciNet  Google Scholar 

  25. Lee, T.S.: Image representation using 2D Gabor wavelets. IEEE Trans. Pattern Anal. Mach. Intell. 18, 959–971 (1996)

    Article  Google Scholar 

  26. Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Process. 11, 467–476 (2002)

    Article  Google Scholar 

  27. Yang, J., Zhang, D., Frangi, A.F., Yang, J.Y.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26, 131–137 (2004)

    Article  Google Scholar 

  28. Li, M., Yuan, B.: 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recogn. Lett. 26, 527–532 (2005)

    Article  Google Scholar 

  29. Phillips, P., Blackburn, D., Bone, M., Grother, P., Micheals, R., Tabassi, E.: Face recogntion vendor test (2002). http://www.frvt.org

  30. Makihara, Y., Mannami, H., Tsuji, A., Hossain, M., Sugiura, K., Mori, A., Yagi, Y.: The ou-isir gait database comprising the treadmill dataset. IPSJ Trans. Comput. Vis. Appl. 4, 53–62 (2012)

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported by JSPS Grants-in-Aid for Scientific Research (A) JP15H01693, by Jiangsu Provincial Science and Technology Support Program (No. BE2014714), by the 111 Project (No. B13022), and by the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Chi Xu, Xiang Li & Jianfeng Lu

  2. Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan

    Chi Xu, Yasushi Makihara, Xiang Li & Yasushi Yagi

Authors
  1. Chi Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yasushi Makihara
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yasushi Yagi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jianfeng Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yasushi Makihara .

Editor information

Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Xu, C., Makihara, Y., Li, X., Yagi, Y., Lu, J. (2017). Speed Invariance vs. Stability: Cross-Speed Gait Recognition Using Single-Support Gait Energy Image. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10112. Springer, Cham. https://doi.org/10.1007/978-3-319-54184-6_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54184-6_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54183-9

  • Online ISBN: 978-3-319-54184-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation