DTW-based kernel and rank-level fusion for 3D gait recognition using Kinect


This paper presents a new 3D gait recognition method that utilizes the kinect skeleton data for representing the gait signature. We propose to use two new features, namely joint relative distance (JRD) and joint relative angle (JRA), which are robust against view and pose variations. The relevance of each JRD and JRA sequence in representing human gait is evaluated using a genetic algorithm. We also introduce a dynamic time warping-based kernel that takes a collection of JRD or JRA sequences as parameters and computes a dissimilarity measure between the training and the unknown sample. The proposed kernel can effectively handle variable walking speed without any need of extra pre-processing. In addition, we propose a rank-level fusion of JRD and JRA features that can boost the overall recognition performance greatly. The effectiveness of the proposed method is evaluated using a 3D skeletal gait database captured with a Kinect v2 sensor. In our experiments, rank level fusion of joint relative distance (JRD) and joint relative angle (JRA) achieves promising results, as compared against only JRD and only JRA-based gait recognition.

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  1. 1.

    Deutschmann, I., Nordstrom, P., Nilsson, L.: Continuous authentication using behavioral biometrics. IT Prof. 15(4), 12–15 (2013)

    Article  Google Scholar 

  2. 2.

    Zhou, X., Bhanu, B.: Integrating face and gait for human recognition at a distance in video. IEEE Trans. Syst. Man. Cybern. Part B Cybern. 37(5), 1119–1137 (2007)

    Article  Google Scholar 

  3. 3.

    Prabhakar, S., Pankanti, S., Jain, A.K.: Biometric recognition: security and privacy concerns. IEEE Secur. Privacy 1(2), 33–42 (2003)

    Article  Google Scholar 

  4. 4.

    Munsell, B.C., Temlyakov, A, Qu, C., Wang, S.: Person identification using full-body motion and anthropometric biometrics from kinect videos. In: Proc. European Conf. on Computer Vision Ws/Demos, LNCS 7585, pp. 91–100 (2012)

  5. 5.

    Zhang, Y., Zheng, J., Magnenat-Thalmann, N.: Example-guided anthropometric human body modeling. The Visual Computer (CGI 2014), pp. 1–17 (2014)

  6. 6.

    Bae, M.S., Park, K.: Content-based 3D model retrieval using a single depth image from a low-cost 3D camera. Visual Comput. 29, 555–564 (2013)

    Article  Google Scholar 

  7. 7.

    Zhou, L., Zhiwu, L., Leung, H., Shang, L.: Spatial temporal pyramid matching using temporal sparse representation for human motion retrieval. Visual Comput. 30, 845–854 (2014)

    Article  Google Scholar 

  8. 8.

    Das Choudhury, S., Guan, Y., Chang-Tsun, L.: Gait recognition using low spatial and temporal resolution videos. In: Proc. Intl. Work. on Biometrics and Forensics, pp. 1–6 (2014)

  9. 9.

    Wang, L., Tan, T., Ning, H., Hu, W.: Silhouette analysis-based gait recognition for human identification. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1505–1518 (2003)

    Article  Google Scholar 

  10. 10.

    Han, J., Bhanu, B.: Statistical Feature Fusion for Gait-based Human Recognition. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition. 2, pp. 842–847 (2004)

  11. 11.

    Wang, J., She, M., Nahavandi, S., Kouzani, A.: A review of vision-based gait recognition methods for human identification. In: Proc. IEEE Intl. Conf. on Digital Image Computing: Techniques and Application, pp. 320–327 (2010)

  12. 12.

    BenAbdelkader, C., Cutler, R., Davis, L.: Stride and cadence as a biometric in automatic person identification and verification. In: Proc. IEEE Intl. Conf. on Automatic Face and Gesture Recognition, pp. 372–377 (2002)

  13. 13.

    Urtasun, R., Fua, P.: 3D Tracking for Gait Characterization and Recognition. In: Proc. IEEE Intl. Conf. on Automatic Face and Gesture Recognition, pp. 17–22 (2004)

  14. 14.

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

    Article  Google Scholar 

  15. 15.

    Sinha, A., Chakravarty, K., Bhowmick, B.: Person Identification using skeleton Information from Kinect. In: Proc. Intl. Conf. on Advances in Computer-Human Interactions, pp. 101–108 (2013)

  16. 16.

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

    Article  Google Scholar 

  17. 17.

    Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23, 257–267 (2001)

    Article  Google Scholar 

  18. 18.

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

    Article  Google Scholar 

  19. 19.

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

    Google Scholar 

  20. 20.

    Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth image. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1297–1304 (2011)

  21. 21.

    Stone, E.E., Skubic, M.: Evaluation of an inexpensive depth camera for passive in-home fall risk assessment. In: Proc. Intl. Pervasive Computing Technologies for Healthcare Conf., pp. 71–77 (2011)

  22. 22.

    Chang, Y.J., Chen, S.F., Huang, J.D.: A Kinect-based system for physical rehabilitation: a pilot study for young adults with motor disabilities. Res. Dev. Disabil. 32(6), 2566–2570 (2011)

    Article  Google Scholar 

  23. 23.

    Popa, M., Koc, A.K., Rothkrantz, L.J.M., Shan, C., Wiggers, P.: Kinect sensing of shopping related actions. Commun. Comput. Inf. Sci. 277, 91–100 (2012)

    Google Scholar 

  24. 24.

    Ball, A., Rye, D., Ramos, F., Velonaki, M.: Unsupervised clustering of people from ’Skeleton’ Data. In: Proc. ACM/IEEE Intl. Conf. on Human Robot Interaction, pp. 225–226 (2012)

  25. 25.

    Preis J., Kessel M., Linnhoff-Popien C., Werner M.: Gait recognition with kinect. In: Proc. Work. on Kinect in Pervasive Computing (2012)

  26. 26.

    Gabel, M., Gilad-Bachrach, R., Renshaw, E., Schuster, A.: Full body gait analysis with Kinect. In: Proc. Annual Intl. Conf. of the IEEE Engineering in Medicine and Biology Society, pp. 1964–1967 (2012)

  27. 27.

    Kinect for windows features: http://www.microsoft.com/en-us/kinectforwindows/meetkinect/features.aspx. Accessed 22 Apr 2015

  28. 28.

    Kale, A., Sundaresan, A., Rajagopalan, A.N., Cuntoor, N.P., Roy-Chowdhury, A.K., Kruger, V., Chellapa, R.: Identification of humans using Gait. IEEE Trans. Image Process. 13(9), 1163–1173 (2004)

    Article  Google Scholar 

  29. 29.

    Sarkar, S., Phillips, P.J., Liu, Z., Vega, I.R., Grother, P., Bowyer, K.W.: The humanID gait challenge problem: data sets, performance, and analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27(2), 162–177 (2005)

    Article  Google Scholar 

  30. 30.

    Tang, J.K., Leung, H.: Retrieval of logically relevant 3D human motions by Adaptive Feature Selection with Graded Relevance Feedback. Pattern Recogn. Lett. 33, 420–430 (2012)

    Article  Google Scholar 

  31. 31.

    Tang, J.K., Leung, H., Komura, T., Shum, H.P.: Emulating human perception of motion similarity. Comput. Animat. Virtual Worlds 19, 211–221 (2008)

    Article  Google Scholar 

  32. 32.

    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, USA (1975)

    Google Scholar 

  33. 33.

    Kruskal, J.B., Liberman, M.: The symmetric time-warping problem: from continuous to discrete. In: Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparisons. Addison-Wesley, Reading, Massachusetts (1983)

  34. 34.

    Shanker, A.P., Rajagopalan, A.N.: Off-line signature verification using DTW. Pattern Recogn. Lett. 28, 1407–1414 (2007)

    Article  Google Scholar 

  35. 35.

    Kumar, A., Shekhar, S.: Palmprint recognition using rank level fusion. IEEE Intl. Conf. on Image Processing, 3121–3124 (2010)

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The authors would like to thank NSERC DISCOVERY program, URGC, NSERC ENGAGE, AITF, and SMART Technologies ULC, Canada for partial support.

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Correspondence to Faisal Ahmed.

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Ahmed, F., Paul, P.P. & Gavrilova, M.L. DTW-based kernel and rank-level fusion for 3D gait recognition using Kinect. Vis Comput 31, 915–924 (2015). https://doi.org/10.1007/s00371-015-1092-0

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  • Gait recognition
  • Kinect v2 sensor
  • Joint relative distance
  • Joint relative angle
  • DTW-kernel
  • 3D skeleton