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A Survey of Advances in Biometric Gait Recognition

  • Zhaoxiang Zhang
  • Maodi Hu
  • Yunhong Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7098)

Abstract

Biometric gait analysis is to acquire biometric information such as identity, gender, ethnicity and age from people walking patterns. In the walking process, the human body shows regular periodic motion, especially upper and lower limbs, which reflects the individual’s unique movement pattern. Compared to other biometrics, gait can be obtained from distance and is difficult to hide and camouflage. During the past ten years, gait has been a hot topic in computer vision with great progress achieved. In this paper, we give a general review and a simple survey of recent gait progresses.

Keywords

Gait Analysis Biometric Recognition 

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References

  1. 1.
    Murray, M.P., Drought, A.B., Kory, R.C.: Walking patterns of normal men. Journal of Bone Join Surgery 46(2), 335–360 (1964)CrossRefGoogle Scholar
  2. 2.
    Murray, M.P.: Gait as a total pattern of movement. American Journal of Physical Medicine 46(1), 290–333 (1967)Google Scholar
  3. 3.
    Ralston, H.J., Inman, V., Todd, E.: Human walking. Williams and Wilkins (1981)Google Scholar
  4. 4.
    Johansson, G.: Visual perception of biological motion and a model for its analysis. Perception and Psychophysics 14(2), 201–211 (1977)CrossRefGoogle Scholar
  5. 5.
    Cutting, J.E., Kozlowski, L.T.: Recognizing friends by their walk: gait perception without familiarity cues. Bulletin of the Psychonomic Society 9(5), 353–356 (1977)CrossRefGoogle Scholar
  6. 6.
    van Doornikc, J., Sinkjaer, T.: Robotic platform for human gait analysis. IEEE Trans. Biomed. Eng. 54(9), 1696–1702 (2007)CrossRefGoogle Scholar
  7. 7.
    Lee, S.W., Mase, K., Kogure, K.: Detection of spatio-temporal gait parameters by using wearable motion sensors. In: Proc. IEEE Conf. on Eng. Med. Biol. Soc., pp. 6836–6839 (2005)Google Scholar
  8. 8.
    Vanitchatchavan, P.: Patterns of joint angles during termination of human gait. In: Proc. IEEE Conf. on Syst., Man, Cybern., pp. 1226–1230 (2000)Google Scholar
  9. 9.
    Barclay, C.D., Cutting, J.E., Kozlowski, L.T.: Temporal and spatial factors in gait perception that influence gender recognition. Perception and Psychophysics 23(2), 145–152 (1978)CrossRefGoogle Scholar
  10. 10.
    Cutting, J.E., Proffitt, D.R., Kozlowski, L.T.: A biochemical invariant for gait perception. Journal of Experimental Psychology: Human Perception and Performance 4, 357–372 (1978)Google Scholar
  11. 11.
    Field, M., Stirling, D., Naghdy, F., Pan, Z.: Mixture model segmentation for gait recognition. In: ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, pp. 3–8 (2008)Google Scholar
  12. 12.
    Yu, S., Tan, D., Tan, T.: A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: Proc. IEEE/IAPR Int. Conf. Pattern Recog., vol. 4, pp. 441–444 (2006)Google Scholar
  13. 13.
    Gross, R., Shi, J.: The cmu motion of body (mobo) database. Robotics Institute, Pittsburgh, PA, Tech. Rep. CMU-RI-TR-01-18 (June 2001)Google Scholar
  14. 14.
    Shutler, J.D., Grant, M.G., Nixon, M.S., Carter, J.N.: On a large sequence-based human gait database. In: Proc. Int. Conf. Recent Advances Soft Comput., pp. 66–72 (2002)Google Scholar
  15. 15.
    Sarkar, S., Phillips, P.J., Liu, Z., Vega, I.R., Grother, P., Bowyer, K.W.: The human id gait challenge problem: Data sets, performance, and analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27(2), 162–177 (2005)CrossRefGoogle Scholar
  16. 16.
    Zhang, D., Wang, Y.: Investigating the separability of features from different views for gait based gender classification. In: Proc. IEEE/IAPR Int. Conf. Pattern Recog., pp. 1–4 (2008)Google Scholar
  17. 17.
    Ran, Y., Weiss, I., Zheng, Q., Davis, L.S.: Pedestrian detection via periodic motion analysis. Int. J. Comput. Vis. 2(71), 143–160 (2007)CrossRefGoogle Scholar
  18. 18.
    Jean, F., Albu, A.B., Bergevin, R.: Towards view-invariant gait modeling: Computing view-normalized body part trajectories. Pattern Recog. 42(11), 2936–2949 (2009)CrossRefzbMATHGoogle Scholar
  19. 19.
    Gu, J., Ding, X., Wang, S., Wu, Y.: Action and gait recognition from recovered 3-d human joints. IEEE Trans. Syst., Man, Cybern. B 40(4), 1021–1033 (2010)CrossRefGoogle Scholar
  20. 20.
    Boulgouris, N.V., Chi, Z.X.: Gait recognition using radon transform and linear discriminant analysis. IEEE Trans. Image Process. 16(3), 857–860 (2007)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Yu, S., Tan, T., Huang, K., Jia, K., Wu, X.: A study on gait-based gender classification. IEEE Trans. Image Process. 18(8), 1905–1910 (2009)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Hu, M., Wang, Y., Zhang, Z., Wang, Y.: Combining spatial and temporal information for gait based gender classification. In: Proc. IEEE/IAPR Int. Conf. Pattern Recog., pp. 3679–3682 (August 2010)Google Scholar
  23. 23.
    Venkat, I., DeWilde, P.: Robust gait recognition by learning and exploiting sub-gait characteristics. Int. J. Comput. Vis. 91(1), 7–23 (2011)CrossRefzbMATHGoogle Scholar
  24. 24.
    Kwon, K.S., Park, S.H., Kim, E.Y., Kim, H.J.: Human shape tracking for gait recognition using active contours with mean shift. In: Proc. Int. Conf. Human-Comput. Interaction, pp. 690–699 (2007)Google Scholar
  25. 25.
    Bashir, K., Xiang, T., Gong, S.: Gait representation using flow fields. In: Proc. British Mach. Vis. Conf. (2009)Google Scholar
  26. 26.
    Ho, M.-F., Chen, K.-Z., Huang, C.-L.: Gait analysis for human walking paths and identities recognition. In: Proc. Int. Conf. Multimedia Expo., pp. 1054–1057 (2009)Google Scholar
  27. 27.
    Chen, C., Zhang, J., Fleischer, R.: Distance approximating dimension reduction of riemannian manifolds. IEEE Trans. Syst., Man, Cybern. B 40(1), 208–217 (2010)CrossRefGoogle Scholar
  28. 28.
    Kellokumpu, V., Zhao, G., Li, S.Z., Pietikainen, M.: Dynamic texture based gait recognition. In: Proc. IAPR/IEEE Int. Conf. Biometrics, pp. 1000–1009 (2009)Google Scholar
  29. 29.
    Ran, Y., Zheng, Q., Chellappa, R., Strat, T.M.: Applications of a simple characterization of human gait in surveillance. IEEE Trans. Syst., Man, Cybern. B 40(4), 1009–1020 (2010)CrossRefGoogle Scholar
  30. 30.
    Bissacco, A., Soatto, S.: Hybrid dynamical models of human motion for the recognition of human gaits. Int. J. Comput. Vis. 85(1), 101–114 (2009)CrossRefGoogle Scholar
  31. 31.
    Zhang, X., Fan, G.: Dual gait generative models for human motion estimation from a single camera. IEEE Trans. Syst., Man, Cybern. B 40(4), 1034–1049 (2010)CrossRefGoogle Scholar
  32. 32.
    Trivinoa, G., Alvarez-Alvareza, A., Bailadorb, G.: Application of the computational theory of perceptions to human gait pattern recognition. Pattern Recog. 43(7), 2572–2581 (2010)CrossRefGoogle Scholar
  33. 33.
    Hu, M., Wang, Y., Zhang, Z., Zhang, D.: Multi-view multi-stance gait identification. In: Proc. IEEE Int. Conf. Image Process. (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Zhaoxiang Zhang
    • 1
  • Maodi Hu
    • 2
  • Yunhong Wang
    • 3
  1. 1.Laboratory of Intelligent Recognition and Image ProcessingBeihang UniversityBeijingChina
  2. 2.Beijing Key Laboratory of Digital MediaBeihang UniversityBeijingChina
  3. 3.School of Computer Science and EngineeringBeihang UniversityBeijingChina

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