Human Gait Recognition Using Body Measures and Joint Angles (original) (raw)

Abstract

Gait is the walking style or pattern of the human motions. This research aims to improve the accuracy of human gait recognition using the information provided by Microsoft Kinect. It benefits from a human joint positioning system by Kinect in three dimensions and proposes a new method in recognising the human gait. In this method, the angles between the body’s joints during the walking process have been used in addition to the body’s limb dimensions and lengths. The result of the experiments has proved that this method can provide higher accuracy in recognising the human gait in comparison to the previous method.

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References (24)

  1. REFERENCES
  2. J. E. Boyd and J. J. Little, "Biometric gait recognition," in Advanced Studies in Biometrics, ed: Springer, 2005, pp. 19-42.
  3. \K. Khoshelham, "Accuracy analysis of kinect depth data," in ISPRS workshop laser scanning, 2011, p. 1.
  4. J. Preis, M. Kessel, M. Werner, and C. Linnhoff-Popien, "Gait recognition with kinect," in 1st International Workshop on Kinect in Pervasive Computing, 2012.
  5. M. Gabel, R. Gilad-Bachrach, E. Renshaw, and A. Schuster, "Full body gait analysis with Kinect," in Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, 2012, pp. 1964-1967.
  6. E. E. Stone and M. Skubic, "Passive in- home measurement of stride-to-stride gait variability comparing vision and Kinect sensing," in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, 2011, pp. 6491-6494.
  7. E. Spinella, "Biometric Scanning Technologies: Finger, Facial and Retinal Scanning," SANS Institute, San Francisco, CA, vol. 28, 2003.
  8. S. Kaur and E. R. K. Sandhu, "A Survey on Enhanced Human Identification Using Gait Recognition based on Neural Network And Support Vector Machine," International Journal of Application or Innovation in Engineering & Management (IJAIEM), Web Site: www. ijaiem. org Email: editor@ ijaiem. org, editorijaiem@ gmail. com, vol. 2, 2013.
  9. L. Lee and W. E. L. Grimson, "Gait analysis for recognition and classification," in Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE International Conference on, 2002, pp. 148-155.
  10. G. Johansson, "Visual perception of biological motion and a model for its analysis," Perception & psychophysics, vol. 14, pp. 201-211, 1973.
  11. A. Y. Johnson and A. F. Bobick, "A multi- view method for gait recognition using static body parameters," in Audio-and Video-Based Biometric Person Authentication, 2001, pp. 301-311.
  12. D. Gafurov, "A survey of biometric gait recognition: Approaches, security and challenges," in Annual Norwegian Computer Science Conference, 2007, pp. 19-21.
  13. Z. Liu and S. Sarkar, "Improved gait recognition by gait dynamics normalization," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 28, pp. 863-876, 2006.
  14. J. Han and B. Bhanu, "Individual recognition using gait energy image," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 28, pp. 316-322, 2006.
  15. J. Lu, G. Wang, and P. Moulin, "Human Identity and Gender Recognition from Gait Sequences with Arbitrary Walking Directions," 2013.
  16. R. J. Orr and G. D. Abowd, "The smart floor: a mechanism for natural user identification and tracking," in CHI'00 extended abstracts on Human factors in computing systems, 2000, pp. 275-276.
  17. S. J. Morris and J. A. Paradiso, "Shoe- integrated sensor system for wireless gait analysis and real-time feedback," in Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES
  18. Conference, 2002. Proceedings of the Second Joint, 2002, pp. 2468-2469.
  19. H. J. Ailisto, M. Lindholm, J. Mantyjarvi, E. Vildjiounaite, and S.-M. Makela, "Identifying people from gait pattern with accelerometers," in Defense and Security, 2005, pp. 7-14.
  20. R. Tanawongsuwan and A. Bobick, "Gait recognition from time-normalized joint- angle trajectories in the walking plane," in Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, 2001, pp. II-726-II-731 vol. 2.
  21. C. BenAbdelkader, R. Cutler, H. Nanda, and L. Davis, "Eigengait: Motion-based recognition of people using image self- similarity," in Audio-and Video-Based Biometric Person Authentication, 2001, pp. 284-294.
  22. J. E. Boyd, "Video phase-locked loops in gait recognition," in Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, 2001, pp. 696-703.
  23. M. Kumar and R. V. Babu, "Human gait recognition using depth camera: a covariance based approach," in Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing, 2012, p. 20.
  24. A. Ball, D. Rye, F. Ramos, and M. Velonaki, "Unsupervised clustering of people from'skeleton'data," in Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction, 2012, pp. 225-226.