Skip to main content
Springer Nature Link
Log in

Multi-view gait recognition using NMF and 2DLDA

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

View Transformation Model(VTM) is extensively employed in multi-view gait recognition. However, there still exists decline of matching accuracy among view transformation procedures. Particularly, the loss grows rapidly with the increase of the disparity of views. In the face of this difficulty, firstly, Non-negative Matrix Factorization(NMF) is introduced to obtain local structured features of human body to compensate accuracy loss. Moreover, 2D Linear Discriminant Analysis(2DLDA) is applied to improve classification ability by projecting features into a discriminant space. In the end, gait features, the Gait Energy Images(GEIs), is strengthened as 2D Enhanced GEI(2D-EGEI) by using the reconstruction of 2D Principal Component Analysis(2DPCA). Compared with the state-of-the-art, proposed method significantly outperforms the others. Furthermore, the comparisons of two deep learning methods is evaluated as well. Experimental outcomes show that the proposed method provides an alternative way to obtain the approximative outcomes compared with the deep learning methods.

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

Access this article

Log in via an institution

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Ariyanto G, Nixon MS (2011) Model-based 3d gait biometrics. In: International joint conference on biometrics, pp 1–7

  2. Ben X, Zhang P, Lai Z, Yan R, Zhai X, Meng W (2019) A general tensor representation framework for cross-view gait recognition. Pattern Recogn 90:87–98

    Article  Google Scholar 

  3. Berry MW, Brown M, Langvill AN, Pauca VP, Plemmons RJ (2007) Algorithms and applications for approximate nonnegative matrix factorization. Comput Stat Data Anal 52(1):155–173

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  5. Choudhury SD, Tjahjadi T (2015) Robust view-invariant multiscale gait recognition. Pattern Recogn 48(3):798–811

    Article  Google Scholar 

  6. He Y, Zhang J, Shan H, Liang W (2019) Multi-task gans for view-specific feature learning in gait recognition. IEEE Trans Inf Forensics Secur 14(1):102–113

    Article  Google Scholar 

  7. Iwama H, Okumura M, Makihara Y, Yagi Y (2012) The ou-isir gait database comprising the large population dataset and performance evaluation of gait recognition. IEEE Trans Inf Forensics Secur 7(5):1511–1521

    Article  Google Scholar 

  8. Jean F, Bergevin R, Albu AB (2009) Computing and evaluating view-normalized body part trajectories. Image Vis Comput 27(9):1272–1284

    Article  Google Scholar 

  9. Ju Han BB (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28(2):316–322

    Article  Google Scholar 

  10. Kale A, Chowdhury AKR, Chellappa R (2003) Towards a view invariant gait recognition algorithm. In: 2003. Proceedings. IEEE conference on advanced video and signal based surveillance, pp 143–150

  11. Kusakunniran W, Wu Q, Li H, Zhang J (2009) Multiple views gait recognition using view transformation model based on optimized gait energy image. In: IEEE international conference on computer vision workshops, pp 1058–1064

  12. Kusakunniran W, Wu Q, Zhang J, Li H (2010) Support vector regression for multi-view gait recognition based on local motion feature selection. In: Computer vision and pattern recognition, pp 974–981

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

    Article  Google Scholar 

  14. Kusakunniran W, Wu Q, Zhang J, Ma Y, Li H (2013) A new view-invariant feature for cross-view gait recognition. IEEE Trans Inf Forensics Secur 8(10):1642–1653

    Article  Google Scholar 

  15. Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788

    Article  Google Scholar 

  16. Lee DD, Seung HS (2000) Algorithms for non-negative matrix factorization. In: NIPS, pp 556–562

  17. Liu X-Z, Ye H-W (2015) Dual-kernel based 2d linear discriminant analysis for face recognition. J Ambient Intell Humaniz Comput 6(5):557–562

    Article  Google Scholar 

  18. Makihara Y, Sagawa R, Mukaigawa Y, Echigo T, Yagi Y (2006) Gait recognition using a view transformation model in the frequency domain. In: European conference on computer vision, pp 151–163

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

    Article  Google Scholar 

  20. Muramatsu D, Makihara Y, Yagi Y (2016) View transformation model incorporating quality measures for cross-view gait recognition. IEEE Transactions on Cybernetics 46(7):1602–1615

    Article  Google Scholar 

  21. Nixon MS, Tan T, Chellappa R (2006) Human identification based on gait, Springer US

  22. Semwal VB, Kumar C, Mishra PK, Nandi GC (2016) Design of vector field for different subphases of gait and regeneration of gait pattern. IEEE Trans Autom Sci Eng 15(1):1–7

    Google Scholar 

  23. Shakhnarovich G, Lee L, Darrell T (2001) Integrated face and gait recognition from multiple views. In: CVPR 2001. Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition, 2001, vol 1, pp I–439–I–446

  24. Shiraga K, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2016) Geinet: view-invariant gait recognition using a convolutional neural network. In: International conference on biometrics, pp 1–8

  25. Takemura N, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2017) On input/output architectures for convolutional neural network-based cross-view gait recognition. IEEE Trans Circuits Syst Video Technol PP(99):1–1

    Google Scholar 

  26. Tong SB, Fu YZ, Ling HF (2019) Cross-view gait recognition based on a restrictive triplet network. Pattern Recogn Lett 125:212–219

    Article  Google Scholar 

  27. Wolf T, Babaee M, Rigoll G (2016) Multi-view gait recognition using 3d convolutional neural networks. In: IEEE international conference on image processing, pp 4165–4169

  28. Wu Z, Huang Y, Wang L, Wang X, Tan T (2016) A comprehensive study on cross-view gait based human identification with deep cnns. IEEE Trans Pattern Anal Mach Intell PP(99):1–1

    Google Scholar 

  29. Wu C, Song Y, Zhang Y (2018) Multi-view gait recognition using 2d-egei and nmf. In: 2018 IEEE 4th international conference on identity, security, and behavior analysis (ISBA), pp 1–8

  30. Yang J, Zhang D, Frangi AF, Yang J (2004) Two-dimensional pca: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131

    Article  Google Scholar 

  31. Yu S, Chen H, Wang Q, Shen L, Huang Y (2017) Invariant feature extraction for gait recognition using only one uniform model. Neurocomputing 239 (C):81–93

    Article  Google Scholar 

  32. Zhang D, Chen S, Zhou ZH (2005) Two-dimensional non-negative matrix factorization for face representation and recognition. Springer, Berlin

    Book  Google Scholar 

  33. Zheng S, Zhang J, Huang K, He R, Tan T (2011) Robust view transformation model for gait recognition. In: International conference on image processing (ICIP), 2011, Brussels, Belgium, pp 2073–2076

Download references

Acknowledgements

An earlier version of this paper was presented at the 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA) [29]. This work is partially supported by the National Key Research and Development Project of China (2017YFB1301101), and the Natural Science Basic Research Plan in Shaanxi Province of China (Program No.2018JM6104).

Author information

Authors and Affiliations

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

    Chen Wu

  2. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, China

    Yonghong Song & Yuanlin Zhang

Authors
  1. Chen Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yonghong Song
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yuanlin Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Yonghong Song.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, C., Song, Y. & Zhang, Y. Multi-view gait recognition using NMF and 2DLDA. Multimed Tools Appl 78, 35789–35811 (2019). https://doi.org/10.1007/s11042-019-08153-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08153-4

Keywords