Skip to main content
SpringerLink
Log in

Gait-based person re-identification under covariate factors

  • Industrial and commercial application
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Gait is recognized as an effective behavioral biometric trait. Gait pattern information can be captured and perceived from a distance thanks to its noninvasive and less intrusive nature. Therefore, gait could be well suited for person re-identification. However, semantic information like clothing and carrying bags has a remarkable influence on its accuracy. Unlike the existing solutions, this paper proposed a new method for gait-based person re-identification relying on dynamic selection of human parts. This method consists in computing a new person descriptor from relevant selected human parts. The selection of the most informative parts was achieved depending on the presence of semantic information. Our experiments were performed on the CASIA-B database revealing promising results and showing the effectiveness of the proposed method.

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

Access this article

Log in via an institution

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Alotaibi M, Mahmood A (2017) Reducing covariate factors of gait recognition using feature selection and dictionary-based sparse coding. Signal Image Video Process 11(6):1131–1138

    Article  Google Scholar 

  2. An L, Chen X, Kafai M, Yang S, Bhanu B (2013) Improving person re-identification by soft biometrics based reranking. In: 2013 7th international conference on distributed smart cameras (ICDSC). IEEE, pp 1–6

  3. Arora P, Srivastava S et al (2016) Human gait recognition using gait flow image and extension neural network. In: Proceedings of the 2nd international conference on computer and communication technologies. Springer, Berlin, pp 1–10

  4. Bashir K, Xiang T, Gong S (2010) Gait recognition without subject cooperation. Pattern Recognit Lett 31(13):2052–2060

    Article  Google Scholar 

  5. Bedagkar-Gala A, Shah SK (2014) Gait-assisted person re-identification in wide area surveillance. In: Asian conference on computer vision. Springer, Berlin, pp 633–649

  6. Benouis M, Senouci M, Tlemsani R, Mostefai L (2016) Gait recognition based on model-based methods and deep belief networks. Int J Biomet 8(3–4):237–253

    Article  Google Scholar 

  7. Binsaadoon AG, El-Alfy ESM (2016) Gait-based recognition for human identification using fuzzy local binary patterns. In: ICAART (2), pp 314–321

  8. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the 5th annual workshop on computational learning theory. ACM, pp 144–152

  9. Chapelle O, Keerthi SS (2010) Efficient algorithms for ranking with svms. Inf Retr 13(3):201–215

    Article  Google Scholar 

  10. Chen C, Liang J, Zhao H, Hu H, Tian J (2009) Frame difference energy image for gait recognition with incomplete silhouettes. Pattern Recognit Lett 30(11):977–984

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Cunado D, Nixon MS, Carter JN (2003) Automatic extraction and description of human gait models for recognition purposes. Comput Vis Image Underst 90(1):1–41

    Article  Google Scholar 

  13. Dempster WT, Gaughran GR (1967) Properties of body segments based on size and weight. Dev Dyn 120(1):33–54

    Google Scholar 

  14. Dupuis Y, Savatier X, Vasseur P (2013) Feature subset selection applied to model-free gait recognition. Image Vis Comput 31(8):580–591

    Article  Google Scholar 

  15. Gabriel-Sanz S, Vera-Rodriguez R, Tome P, Fierrez J (2013) Assessment of gait recognition based on the lower part of the human body. In: 2013 international workshop on biometrics and forensics (IWBF). IEEE, pp 1–4

  16. Gray D, Tao H (2008) Viewpoint invariant pedestrian recognition with an ensemble of localized features. Comput Vis ECCV 2008:262–275

    Google Scholar 

  17. Gu J, Ding X, Wang S, Wu Y (2010) Action and gait recognition from recovered 3-D human joints. IEEE Trans Syst Man Cybern Part B (Cybern) 40(4):1021–1033

    Article  Google Scholar 

  18. Hosseini NK, Nordin MJ (2013) Human gait recognition: a silhouette based approach. J Autom Control Eng 1(2):259–267

    Google Scholar 

  19. 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 

  20. Iwashita Y, Uchino K, Kurazume R (2013) Gait-based person identification robust to changes in appearance. Sensors 13(6):7884–7901

    Article  Google Scholar 

  21. Khalid B, Tao X, Shaogang G (2009) Gait recognition using gait entropy image. In: 3rd international conference on crime detection and prevention (ICDP 2009)

  22. Khedher MI (2014) Ré-identification de personnes à partir des séquences vidéo. PhD thesis, Institut National des Télécommunications

  23. Kovač J, Peer P (2014) Human skeleton model based dynamic features for walking speed invariant gait recognition. Math Probl Eng 2014:15

    Article  Google Scholar 

  24. Kumar HM, Nagendraswamy H (2014) LBP for gait recognition: a symbolic approach based on GEI plus RBL of GEI. In: 2014 international conference on electronics and communication systems (ICECS). IEEE, pp 1–5

  25. Kusakunniran W (2014) Attribute-based learning for gait recognition using spatio-temporal interest points. Image Vis Comput 32(12):1117–1126

    Article  Google Scholar 

  26. Kusakunniran W, Wu Q, Li H, Zhang J (2009) Automatic gait recognition using weighted binary pattern on video. In: 6th IEEE international conference on advanced video and signal based surveillance, 2009. AVSS’09. IEEE, pp 49–54

  27. Lam TH, Cheung KH, Liu JN (2011) Gait flow image: a silhouette-based gait representation for human identification. Pattern Recognit 44(4):973–987

    Article  MATH  Google Scholar 

  28. Layne R, Hospedales TM, Gong S (2014) Attributes-based re-identification. In: Person re-identification. Springer, Berlin, pp 93–117

  29. Lee CP, Tan AW, Tan SC (2015) Gait recognition with transient binary patterns. J Vis Commun Image Represent 33:69–77

    Article  Google Scholar 

  30. Li N, Xu Y, Yang XK (2010) Part-based human gait identification under clothing and carrying condition variations. In: 2010 international conference on machine learning and cybernetics (ICMLC), vol 1. IEEE, pp 268–273

  31. Li X, Chen Y (2013) Gait recognition based on structural gait energy image. J Comput Inf Syst 9(1):121–126

    Google Scholar 

  32. Liang Y, Li CT, Guan Y, Hu Y (2016) Gait recognition based on the golden ratio. EURASIP J Image Video Process 2016(1):22

    Article  Google Scholar 

  33. Lishani AO, Boubchir L, Khalifa E, Bouridane A (2017) Human gait recognition based on Haralick features. Signal Image Video Process 11:1–8

    Article  Google Scholar 

  34. Liu D, Ye M, Li X, Zhang F, Lin, L.: Memory-based gait recognition. In: BMVC (2016)

  35. Liu W, Liu H, Tao D, Wang Y, Lu K (2015) Multiview hessian regularized logistic regression for action recognition. Signal Process 110:101–107

    Article  Google Scholar 

  36. Liu W, Zha ZJ, Wang Y, Lu K, Tao D (2016) \(p\)-laplacian regularized sparse coding for human activity recognition. IEEE Trans Ind Electron 63(8):5120–5129

    Google Scholar 

  37. Liu Y, Zhang J, Wang C, Wang L (2012) Multiple hog templates for gait recognition. In: 2012 21st international conference on pattern recognition (ICPR). IEEE, pp 2930–2933

  38. Liu Z, Zhang Z, Wu Q, Wang Y (2015) Enhancing person re-identification by integrating gait biometric. Neurocomputing 168:1144–1156

    Article  Google Scholar 

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

    Article  Google Scholar 

  40. Martín-Félez R, Xiang T (2014) Uncooperative gait recognition by learning to rank. Pattern Recognit 47(12):3793–3806

    Article  Google Scholar 

  41. Nandy A, Pathak A, Chakraborty P (2017) A study on gait entropy image analysis for clothing invariant human identification. Multimed Tools Appl 76(7):9133–9167

    Article  Google Scholar 

  42. Nixon M et al (2009) Model-based gait recognition. Encyclopedia of biometrics. Springer, Heidelberg, pp 633–639

  43. Prosser BJ, Zheng WS, Gong S, Xiang T, Mary Q (2010) Person re-identification by support vector ranking. In: BMVC, vol 2, p 6 (2010)

  44. Rafi M, Khammari H, Wahidabanu R, Taj Y (2013) A model based approach for gait recognition system. Int J Soft Comput Eng (IJSCE) 3:2231–2307

    Google Scholar 

  45. Rida I, Almaadeed S, Bouridane A (2016) Gait recognition based on modified phase-only correlation. Signal Image Video Process 10(3):463–470

    Article  Google Scholar 

  46. Rumelhart DE, Hinton GE, Williams RJ et al (1998) Learning representations by back-propagating errors. Cognit Model 5(3):1

    MATH  Google Scholar 

  47. Saadoon A, Nordin MJ (2015) An automatic human gait recognition system based on joint angle estimation on silhouette images. J Theor Appl Inf Technol 81(2):277

    Google Scholar 

  48. Sarkar S, Phillips PJ, Liu Z, Vega IR, Grother P, Bowyer KW (2005) The humanid gait challenge problem: data sets, performance, and analysis. IEEE Trans Pattern Anal Mach Intell 27(2):162–177

    Article  Google Scholar 

  49. Schölkopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge

    Google Scholar 

  50. Sivapalan S, Chen D, Denman S, Sridharan S, Fookes C (2013) Histogram of weighted local directions for gait recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 125–130

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

    Article  Google Scholar 

  52. Wang C, Zhang J, Pu J, Yuan X, Wang L (2010) Chrono-gait image: a novel temporal template for gait recognition. Comput Vis ECCV 2010:257–270

    Google Scholar 

  53. Wei L, Tian Y, Wang Y, Huang T (2015) Swiss-system based cascade ranking for gait-based person re-identification. In: AAAI, pp 1882–1888

  54. Yamauchi K, Bhanu B, Saito H (2009) Recognition of walking humans in 3D: initial results. In: 2009 CVPR Workshops 2009. IEEE computer society conference on computer vision and pattern recognition workshops. IEEE, pp 45–52

  55. Yang X, Liu W, Tao D, Cheng J (2017) Canonical correlation analysis networks for two-view image recognition. Inf Sci 385:338–352

    Article  Google Scholar 

  56. Yegnanarayana B (2009) Artificial neural networks. PHI Learning Pvt. Ltd, New Delhi

    Google Scholar 

  57. Zeng W, Wang C, Li Y (2014) Model-based human gait recognition via deterministic learning. Cognit Comput 6(2):218–229

    Article  Google Scholar 

  58. Yu S, Tan D, Tan T (2006) A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: 18th international conference on pattern recognition, ICPR 2006, vol 4, pp 441–444

  59. Zighed DA, Rakotomalala R (2000) Graphes d’induction: apprentissage et data mining. Hermes Paris, Paris

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. MIRACL Laboratory, FSS, University of Sfax, Road Sokra km 4, BP 802, 3038, Sfax, Tunisia

    Emna Fendri & Mohamed Hammami

  2. MIRACL Laboratory, ENIS, University of Sfax, Road Sokra km 4, BP 1173, 3038, Sfax, Tunisia

    Imen Chtourou

Authors
  1. Emna Fendri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Imen Chtourou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed Hammami
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Imen Chtourou.

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

Fendri, E., Chtourou, I. & Hammami, M. Gait-based person re-identification under covariate factors. Pattern Anal Applic 22, 1629–1642 (2019). https://doi.org/10.1007/s10044-019-00793-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-019-00793-4

Keywords

Search

Navigation