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Multimedia Tools and Applications

, Volume 78, Issue 10, pp 13925–13948 | Cite as

Robust gait identification using Kinect dynamic skeleton data

  • Elena GianariaEmail author
  • Marco Grangetto
Article
  • 70 Downloads

Abstract

Gait has been recently proposed as a biometric feature that, with respect to other human characteristics, can be captured at a distance without requiring the collaboration of the observed subject. Therefore, it turns out to be a promising approach for people identification in several scenarios, e.g. access control and forensic applications. In this paper, we propose an automatic gait recognition system based on a set of features acquired using the 3D skeletal tracking provided by the popular Kinect sensor. Gait features are defined in terms of distances between selected sets of joints and their vertical and lateral sway with respect to walking direction. Moreover we do not rely on any geometrical assumptions on the position of the sensor. The effectiveness of the defined gait features is shown in the case of person identification based on supervised classification, using the principal component analysis and the support vector machine. A rich set of experiments is provided in two scenarios: a controlled identification setup and a classical video-surveillance setting, respectively. Moreover, we investigate if gait can be considered invariant over time for an individual, at least in a time interval of few years, by comparing gait samples of several subjects three years apart. Our experimental analysis shows that the proposed method is robust to acquisition settings and achieves very competitive identification accuracy with respect to the state of the art.

Keywords

Gait recognition Computer vision Biometrics Person identification Microsoft Kinect 

Notes

References

  1. 1.
    Ahmed F, Paul PP, Gavrilova ML (2015) DTW-based kernel and rank-level fusion for 3D gait recognition using Kinect. Vis Comput 31(6–8):915–924CrossRefGoogle Scholar
  2. 2.
    Allard P (1997) Three-dimensional analysis of human locomotion International Society Biomechanics series. Wiley, New YorkGoogle Scholar
  3. 3.
    Andersson V, Araujo R (2015). In: Proceedings of the twenty-ninth association for the advancement of artificial intelligence conference, AAAIGoogle Scholar
  4. 4.
    Ashbourn J (2002) Biometrics - advanced identity verification: the complete guide. Springer, BerlinGoogle Scholar
  5. 5.
    Bolle R, Pankanti S (1998) Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society. Kluwer Academic Publishers, NorwellGoogle Scholar
  6. 6.
    Bouchrika I, Nixon M (2007) In: Computer vision/computer graphics collaboration techniques. Springer, pp 150–160Google Scholar
  7. 7.
    Bouchrika I, Goffredo M, Carter J, Nixon M (2011) J Forensic Sci 56(4):882CrossRefGoogle Scholar
  8. 8.
    Boyd JE, Little JJ (2005) . In: Advanced Studies in Biometrics. Springer, pp 19–42Google Scholar
  9. 9.
    Chattopadhyay P, Sural S, Mukherjee J (2014) IEEE Trans Inf Forensic Secur 9(11):1843CrossRefGoogle Scholar
  10. 10.
    Chattopadhyay P, Sural S, Mukherjee J (2015) Pattern Recogn Lett 63:9CrossRefGoogle Scholar
  11. 11.
    Connie T, Goh MKO, Teoh ABJ (2016) IEEE transactions on cyberneticsGoogle Scholar
  12. 12.
    Cucchiara R, Grana C, Prati A, Vezzani R (2005) In: IEE Proceedings of Vision, Image and Signal ProcessingGoogle Scholar
  13. 13.
    Franc V, Hlavác V (2004) Czech: Center for Machine Perception, Czech Technical University, PragueGoogle Scholar
  14. 14.
    Gianaria E, Balossino N, Grangetto M, Lucenteforte M (2013) In: 2013 IEEE 15th international workshop on multimedia signal processing (MMSP), pp 440–445.  https://doi.org/10.1109/MMSP.2013.6659329
  15. 15.
    Gianaria E, Grangetto M, Lucenteforte M, Balossino N (2014) In: Biometric authentication. Springer, pp 16–27Google Scholar
  16. 16.
    Gianaria E, Grangetto M, Balossino N (2017) In: International conference on image analysis and processing. Springer, pp 648–658Google Scholar
  17. 17.
    Goffredo M, Bouchrika I, Carter J, Nixon M (2010) Multimed Tools Appl 50(1):75.  https://doi.org/10.1007/s11042-009-0378-5 CrossRefGoogle Scholar
  18. 18.
    Han J, Bhanu B (2006) IEEE Trans Pattern Anal Mach Intell 28(2):316CrossRefGoogle Scholar
  19. 19.
    Hegeman J, Shapkova EY, Honegger F, Allum JH (2007) J Vestib Res 17(2):75Google Scholar
  20. 20.
    Jain AK, Ross A, Prabhakar S (2004) IEEE Trans Circ Syst Video Technol 14(1):4CrossRefGoogle Scholar
  21. 21.
    Janssen LJ, Verhoeff LL, Horlings CG, Allum JH (2009) Gait Posture 29(4):575CrossRefGoogle Scholar
  22. 22.
    Jung SU, Nixon M (2012) IEEE Trans Inf Forensic Secur 7(6):1802CrossRefGoogle Scholar
  23. 23.
    Khoshelham K, Elberink SO (2012) Sensors 12(2):1437.  https://doi.org/10.3390/s120201437 CrossRefGoogle Scholar
  24. 24.
    Khoshelham K, Elberink SO (2012) Sensors 12(2):1437CrossRefGoogle Scholar
  25. 25.
  26. 26.
    Kusakunniran W (2014) IEEE Trans Inf Forensic Secur 9(9):1416CrossRefGoogle Scholar
  27. 27.
    Larsen PK, Simonsen EB, Lynnerup N (2007) In: Proceedings of videometrics IX, vol 6491Google Scholar
  28. 28.
    Liao S, Jain AK, Li SZ (2013) IEEE Trans Pattern Anal Mach Intell 35(5):1193CrossRefGoogle Scholar
  29. 29.
    Liu LF, Jia W, Zhu YH (2009) In: Huang DS, Jo KH, Lee HH, Kang HJ, Bevilacqua V (eds) Emerging intelligent computing technology and applications. With Aspects of Artificial Intelligence, Lecture Notes in Computer Science, vol 5755. Springer Berlin Heidelberg, pp 652–659.  https://doi.org/10.1007/978-3-642-04020-7_70
  30. 30.
    Livingston MA, Sebastian J, Ai Z, Decker JW (2012) 2012 IEEE Virtual Reality (VR) 298(0704):119.  https://doi.org/10.1109/VR.2012.6180911 CrossRefGoogle Scholar
  31. 31.
    Muramatsu D, Makihara Y, Yagi Y (2016) IEEE Trans Cybern 46(7):1602CrossRefGoogle Scholar
  32. 32.
    Pala F, Satta R, Fumera G, Roli F (2015) IEEE Trans Circ Syst Video Technol 8215(MARCH):1.  https://doi.org/10.1109/TCSVT.2015.2424056 Google Scholar
  33. 33.
    Preis J, Kessel M, Werner M, Linnhoff-Popien C (2012). In: Proceedings of the first workshop on Kinect in pervasive computingGoogle Scholar
  34. 34.
    Sarkar S, Phillips PJ, Liu Z, Vega IR, Grother P, Bowyer KW (2005) IEEE Trans Pattern Anal Mach Intell 27(2):162.  https://doi.org/10.1109/TPAMI.2005.39 CrossRefGoogle Scholar
  35. 35.
    Satta R, Pala F, Fumera G, Roli F (2013) In: 8th international conference on computer vision theory and applications (VISAPP 2013), BarcelonaGoogle Scholar
  36. 36.
    Schölkopf B, Burges CJ (1999) Advances in kernel methods: support vector learning. MIT Press, CambridgezbMATHGoogle Scholar
  37. 37.
    Tafazzoli F, Safabakhsh R (2010) Eng Appl Artif Intell 23(8):1237CrossRefGoogle Scholar
  38. 38.
    Urtasun R, Fua P (2004) In: 2004 Proceedings sixth IEEE international conference on automatic face and gesture recognition. IEEE, pp 17–22Google Scholar
  39. 39.
    Wang L, Tan T, Ning H, Hu W (2003) IEEE Trans Pattern Anal Mach Intell 25(12):1505CrossRefGoogle Scholar
  40. 40.
    Wang J, She M, Nahavandi S, Kouzani A (2010) In: 2010 international conference on digital image computing: techniques and applications (DICTA). IEEE, pp 320–327Google Scholar
  41. 41.
    Yang K, Dou Y, Lv S, Zhang F, Lv Q (2016) Journal of Visual Communication and Image RepresentationGoogle Scholar
  42. 42.
    Zhang Z (2012) MultiMed IEEE 19(2):4CrossRefGoogle Scholar
  43. 43.
    Zhang Y, Pan G, Jia K, Lu M, Wang Y, Wu Z (2015) IEEE Trans Cybern 45(9):1864CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of TurinTurinItaly

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