Gait-based person re-identification under covariate factors

  • Emna Fendri
  • Imen ChtourouEmail author
  • Mohamed Hammami
Industrial and commercial application


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.


Gait Dynamic selection Re-identification Semantic information 



  1. 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):1131–1138Google Scholar
  2. 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 1–6Google Scholar
  3. 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 1–10Google Scholar
  4. 4.
    Bashir K, Xiang T, Gong S (2010) Gait recognition without subject cooperation. Pattern Recognit Lett 31(13):2052–2060Google Scholar
  5. 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 633–649Google Scholar
  6. 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):237–253Google Scholar
  7. 7.
    Binsaadoon AG, El-Alfy ESM (2016) Gait-based recognition for human identification using fuzzy local binary patterns. In: ICAART (2), pp 314–321Google Scholar
  8. 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 144–152Google Scholar
  9. 9.
    Chapelle O, Keerthi SS (2010) Efficient algorithms for ranking with svms. Inf Retr 13(3):201–215Google Scholar
  10. 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):977–984Google Scholar
  11. 11.
    Choudhury SD, Tjahjadi T (2015) Robust view-invariant multiscale gait recognition. Pattern Recognit 48(3):798–811Google Scholar
  12. 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):1–41Google Scholar
  13. 13.
    Dempster WT, Gaughran GR (1967) Properties of body segments based on size and weight. Dev Dyn 120(1):33–54Google Scholar
  14. 14.
    Dupuis Y, Savatier X, Vasseur P (2013) Feature subset selection applied to model-free gait recognition. Image Vis Comput 31(8):580–591Google Scholar
  15. 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 1–4Google Scholar
  16. 16.
    Gray D, Tao H (2008) Viewpoint invariant pedestrian recognition with an ensemble of localized features. Comput Vis ECCV 2008:262–275Google Scholar
  17. 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):1021–1033Google Scholar
  18. 18.
    Hosseini NK, Nordin MJ (2013) Human gait recognition: a silhouette based approach. J Autom Control Eng 1(2):259–267Google Scholar
  19. 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):1511–1521Google Scholar
  20. 20.
    Iwashita Y, Uchino K, Kurazume R (2013) Gait-based person identification robust to changes in appearance. Sensors 13(6):7884–7901Google Scholar
  21. 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)Google Scholar
  22. 22.
    Khedher MI (2014) Ré-identification de personnes à partir des séquences vidéo. PhD thesis, Institut National des TélécommunicationsGoogle Scholar
  23. 23.
    Kovač J, Peer P (2014) Human skeleton model based dynamic features for walking speed invariant gait recognition. Math Probl Eng 2014:15Google Scholar
  24. 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 1–5Google Scholar
  25. 25.
    Kusakunniran W (2014) Attribute-based learning for gait recognition using spatio-temporal interest points. Image Vis Comput 32(12):1117–1126Google Scholar
  26. 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 49–54Google Scholar
  27. 27.
    Lam TH, Cheung KH, Liu JN (2011) Gait flow image: a silhouette-based gait representation for human identification. Pattern Recognit 44(4):973–987zbMATHGoogle Scholar
  28. 28.
    Layne R, Hospedales TM, Gong S (2014) Attributes-based re-identification. In: Person re-identification. Springer, Berlin, pp 93–117Google Scholar
  29. 29.
    Lee CP, Tan AW, Tan SC (2015) Gait recognition with transient binary patterns. J Vis Commun Image Represent 33:69–77Google Scholar
  30. 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 268–273Google Scholar
  31. 31.
    Li X, Chen Y (2013) Gait recognition based on structural gait energy image. J Comput Inf Syst 9(1):121–126Google Scholar
  32. 32.
    Liang Y, Li CT, Guan Y, Hu Y (2016) Gait recognition based on the golden ratio. EURASIP J Image Video Process 2016(1):22Google Scholar
  33. 33.
    Lishani AO, Boubchir L, Khalifa E, Bouridane A (2017) Human gait recognition based on Haralick features. Signal Image Video Process 11:1–8Google Scholar
  34. 34.
    Liu D, Ye M, Li X, Zhang F, Lin, L.: Memory-based gait recognition. In: BMVC (2016)Google Scholar
  35. 35.
    Liu W, Liu H, Tao D, Wang Y, Lu K (2015) Multiview hessian regularized logistic regression for action recognition. Signal Process 110:101–107Google Scholar
  36. 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):5120–5129Google Scholar
  37. 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 2930–2933Google Scholar
  38. 38.
    Liu Z, Zhang Z, Wu Q, Wang Y (2015) Enhancing person re-identification by integrating gait biometric. Neurocomputing 168:1144–1156Google Scholar
  39. 39.
    Man J, Bhanu B (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28(2):316–322Google Scholar
  40. 40.
    Martín-Félez R, Xiang T (2014) Uncooperative gait recognition by learning to rank. Pattern Recognit 47(12):3793–3806Google Scholar
  41. 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):9133–9167Google Scholar
  42. 42.
    Nixon M et al (2009) Model-based gait recognition. Encyclopedia of biometrics. Springer, Heidelberg, pp 633–639Google Scholar
  43. 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)Google Scholar
  44. 44.
    Rafi M, Khammari H, Wahidabanu R, Taj Y (2013) A model based approach for gait recognition system. Int J Soft Comput Eng (IJSCE) 3:2231–2307Google Scholar
  45. 45.
    Rida I, Almaadeed S, Bouridane A (2016) Gait recognition based on modified phase-only correlation. Signal Image Video Process 10(3):463–470Google Scholar
  46. 46.
    Rumelhart DE, Hinton GE, Williams RJ et al (1998) Learning representations by back-propagating errors. Cognit Model 5(3):1zbMATHGoogle Scholar
  47. 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):277Google Scholar
  48. 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):162–177Google Scholar
  49. 49.
    Schölkopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, CambridgeGoogle Scholar
  50. 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 125–130Google Scholar
  51. 51.
    Tafazzoli F, Safabakhsh R (2010) Model-based human gait recognition using leg and arm movements. Eng Appl Artif Intell 23(8):1237–1246Google Scholar
  52. 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 2010:257–270Google Scholar
  53. 53.
    Wei L, Tian Y, Wang Y, Huang T (2015) Swiss-system based cascade ranking for gait-based person re-identification. In: AAAI, pp 1882–1888Google Scholar
  54. 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 45–52Google Scholar
  55. 55.
    Yang X, Liu W, Tao D, Cheng J (2017) Canonical correlation analysis networks for two-view image recognition. Inf Sci 385:338–352Google Scholar
  56. 56.
    Yegnanarayana B (2009) Artificial neural networks. PHI Learning Pvt. Ltd, New DelhiGoogle Scholar
  57. 57.
    Zeng W, Wang C, Li Y (2014) Model-based human gait recognition via deterministic learning. Cognit Comput 6(2):218–229Google Scholar
  58. 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 441–444Google Scholar
  59. 59.
    Zighed DA, Rakotomalala R (2000) Graphes d’induction: apprentissage et data mining. Hermes Paris, ParisGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.MIRACL Laboratory, FSSUniversity of SfaxSfaxTunisia
  2. 2.MIRACL Laboratory, ENISUniversity of SfaxSfaxTunisia

Personalised recommendations