Skip to main content
SpringerLink
Log in

Multi-view Gait Recognition Method Based on RBF Network

  • Conference paper
  • First Online:
Book cover Biometric Recognition (CCBR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10996))

Included in the following conference series:

  • 3091 Accesses

Abstract

Gait is an important biometrics in human identification, but the view variation problem seriously affects the accuracy of gait recognition. Existing methods for multi-view gait-based identification mainly focus on transforming the features of one view to another view, which might be unsuitable for the real applications. In this paper, we propose a multi-view gait recognition method based on RBF network that employs a unique view-invariant model. First, extracts the gait features by calculating the gait individual image (GII), which could better capture the discriminative information for cross view gait recognition. Then, constructs a joint model, use the DLDA algorithm to project the model and get a projection matrix. Finally, the projected eigenvectors are classified by RBF network. Experiments have been conducted in the CASIA-B database to prove the validity of the proposed method. Experiment results shows that our method performs better than the state-of-the-art multi-view methods.

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

Access this chapter

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

Institutional subscriptions

References

  1. Tafazzoli, F., Safabakhsh, R.: Model-based human gait recognition using leg and arm movements. Eng. Appl. AI 23(8), 1237–1246 (2010)

    Google Scholar 

  2. Yam, C.Y., Nixon, M.S., Carter, J.N.: Automated person recognition by walking and running via model-based approaches. Pattern Recogn. 37(5), 1057–1072 (2004)

    Article  Google Scholar 

  3. Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 316–322 (2006)

    Article  Google Scholar 

  4. Zhang, E., Zhao, Y., Xiong, W.: Active energy image plus 2DLPP for gait recognition. Signal Process. 90(7), 2295–2302 (2010)

    Article  Google Scholar 

  5. Wang, C., Zhang, J., Wang, L.: Human identification using temporal information preserving gait template. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2164–2176 (2012)

    Article  Google Scholar 

  6. Yu, S., Tan, D., Tan, T.: A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: ICPR, vol. 4, pp. 441–444 (2006)

    Google Scholar 

  7. Bodor, R., Drenner, A., Fehr, D.: View-independent human motion classification using image-based reconstruction. Image Vis. Comput. 27(8), 1194–1206 (2009)

    Article  Google Scholar 

  8. Zhang, Z., Troje, N.F.: View-independent person identification from human gait. Neurocomputing 69(1–3), 250–256 (2005)

    Article  Google Scholar 

  9. Zhao, G., Liu, G., Li, H.: 3D gait recognition using multiple cameras. In: FG, pp. 529–534 (2006)

    Google Scholar 

  10. Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., Yagi, Y.: Gait recognition using a view transformation model in the frequency domain. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 151–163. Springer, Heidelberg (2006). https://doi.org/10.1007/11744078_12

    Chapter  Google Scholar 

  11. Kusakunniran, W., Wu, Q., Li, H.: Support vector regression for multi-view gait recognition based on local motion feature selection. In: CVPR, pp. 974–981 (2010)

    Google Scholar 

  12. Kusakunniran, W., Wu, Q., Zhang, J.: Gait recognition under various viewing angles based on correlated motion regression. IEEE Trans. Circ. Syst. Video Technol. 22(6), 966–980 (2012)

    Article  Google Scholar 

  13. Bashir, K., Xiang, T., Gong, S.: Cross view gait recognition using correlation strength. In: BMVC, pp. 1–11 (2010)

    Google Scholar 

  14. Zhang, Z., Chen, J., Wu, Q.: GII representation-based cross-view gait recognition by discriminative projection with list-wise constraints. IEEE Trans. Cybern. 88(99), 1–13 (2017)

    Google Scholar 

  15. Portillo-Portillo, J., Leyva, R., Sanchez, V.: Cross view gait recognition using joint-direct linear discriminant analysis. Sensors 17(1), 6 (2017)

    Google Scholar 

  16. Tao, D., Li, X., Wu, X.: General tensor discriminant analysis and gabor features for gait recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(10), 1700–1715 (2007)

    Article  Google Scholar 

  17. Alldrin, N., Smith, A., Turnbull, D.: Classifying facial expression with radial basis function networks, using gradient descent and K-means (2003)

    Google Scholar 

  18. Yu, S., Wang, Q., Shen, L.: View invariant gait recognition using only one uniform model. In: ICPR, pp. 889–894 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Software Engineering, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, China

    Yaru Qiu

  2. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, China

    Yonghong Song

Authors
  1. Yaru Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yonghong Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yonghong Song .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Jie Zhou

  2. Beihang University, Beijing, China

    Yunhong Wang

  3. Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  4. Xinjiang University, Urumqi, China

    Zhenhong Jia

  5. Tsinghua University, Beijing, China

    Jianjiang Feng

  6. Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

  7. Xinjiang University, Urumqi, China

    Kurban Ubul

  8. Tsinghua University, Shenzhen, China

    Zhenhua Guo

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Qiu, Y., Song, Y. (2018). Multi-view Gait Recognition Method Based on RBF Network. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-97909-0_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97908-3

  • Online ISBN: 978-3-319-97909-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation