Feature Extraction and HMM-Based Classification of Gait Video Sequences for the Purpose of Human Identification

  • Henryk Josiński
  • Daniel Kostrzewa
  • Agnieszka Michalczuk
  • Adam Świtoński
  • Konrad Wojciechowski
Part of the Studies in Computational Intelligence book series (SCI, volume 481)


The authors present results of the research on human recognition based on the video gait sequences from the CASIA Gait Database. Both linear (principal component analysis; PCA) and non-linear (isometric features mapping; Isomap and locally linear embedding; LLE) methods were applied in order to reduce data dimensionality, whereas a concept of hidden Markov model (HMM) was used for the purpose of data classification. The results of the conducted experiments formed the main subject of analysis of classification accuracy expressed by means of the Correct Classification Rate (CCR).


dimensionality reduction gait-based human identification Hidden Markov model manifold learning 


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  1. 1.
    Boyd, J.E., Little, J.J.: Biometric Gait Recognition. In: Tistarelli, M., Bigun, J., Grosso, E. (eds.) Advanced Studies in Biometrics. LNCS, vol. 3161, pp. 19–42. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Nixon, M.S., Tan, T.N., Chellappa, R.: Human Identification Based on Gait. Springer (2006)Google Scholar
  3. 3.
    Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: MPCA: Multilinear Principal Component Analysis of Tensor Objects. IEEE Transactions on Neural Networks 19(1), 18–39 (2008)CrossRefGoogle Scholar
  4. 4.
    Law, M.H.C., Jain, A.K.: Incremental nonlinear dimensionality reduction by manifold learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(3), 377–391 (2006)CrossRefGoogle Scholar
  5. 5.
    Pushpa Rani, M., Arumugam, G.: An Efficient Gait Recognition System for Human Identification Using Modified ICA. International Journal of Computer Science & Information Technology 2(1), 55–67 (2010)Google Scholar
  6. 6.
    Wang, L., Tan, T., Ning, H., Hu, W.: Silhouette Analysis-Based Gait Recognition for Human Identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(12), 1505–1518 (2003)CrossRefGoogle Scholar
  7. 7.
    Ali, H., Dargham, J., Ali, C., Moung, E.G.: Gait Recognition using Principal Component Analysis. In: Proceedings of the 3rd International Conference on Machine Vision, pp. 539–543 (2010)Google Scholar
  8. 8.
    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 Transactions on Pattern Analysis and Machine Intelligence 27(12), 162–177 (2005)CrossRefGoogle Scholar
  9. 9.
    Han, J., Bhanu, B.: Individual Recognition Using Gait Energy Image. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(2), 316–322 (2006)CrossRefGoogle Scholar
  10. 10.
    Wang, L., Ning, H., Hu, W., Tan, T.: Gait Recognition Based on Procrustes Shape Analysis. In: Proceedings of the 9th International Conference on Image Processing, pp. III-433–III436 (2002)Google Scholar
  11. 11.
    Hong, S., Lee, H., Nizami, I.F., An, S.-J., Kim, E.: Human Identification Based on Gait Analysis. In: Proceedings of the International Conference on Control, Automation and Systems, pp. 2234–2237 (2007)Google Scholar
  12. 12.
    Kale, A., Cuntoor, N., Yegnanarayana, B., Rajagopalan, A.N., Chellappa, R.: Gait Analysis for Human Identification. In: Proceedings of the 4th International Conference on Audio- and Video-Based Biometric Person Authentication, pp. 706–714 (2003)Google Scholar
  13. 13.
    Krzeszowski, T., Kwolek, B., Wojciechowski, K.: Articulated Body Motion Tracking by Combined Particle Swarm Optimization and Particle Filtering. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010, Part I. LNCS, vol. 6374, pp. 147–154. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Zhang, Z., Troje, N.F.: View-independent person identification from human gait. Neurocomputing 69, 250–256 (2005)CrossRefGoogle Scholar
  15. 15.
    Świtoński, A., Polański, A., Wojciechowski, K.: Human Identification Based on Gait Paths. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2011. LNCS, vol. 6915, pp. 531–542. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Rabiner, L.R., Juang, H.: An Introduction to Hidden Markov Models. IEEE ASSP Magazine, 4–16 (1986)Google Scholar
  17. 17.
    Kale, A., Rajagopalan, A.N., Cuntoor, N., Krüger, V.: Gait-based Recognition of Humans Using Continuous HMMs. In: Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 336–341 (2002)Google Scholar
  18. 18.
    Sundaresan, A., Roy-Chowdhury, A., Chellappa, R.: A Hidden Markov Model Based Framework for Recognition of Humans from Gait Sequences. In: Proceedings of the 2003 IEEE International Conference on Image Processing, pp. II-93–II-96 (2003)Google Scholar
  19. 19.
    Liu, Z., Sarkar, S.: Improved Gait Recognition by Gait Dynamics Normalization. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(6), 863–876 (2006)CrossRefGoogle Scholar
  20. 20.
    Chen, C., Liang, J., Zhao, H., Hu, H., Tian, J.: Factorial HMM and Parallel HMM for Gait Recognition. IEEE Transactions on Systems, Man, and Cybernetics – Part C: Applications and Reviews 39(1), 114–123 (2009)CrossRefGoogle Scholar
  21. 21.
    Cheng, M.-H., Ho, M.-F., Huang, C.-L.: Gait analysis for human identification through manifold learning and HMM. Pattern Recognition 41(8), 2541–2553 (2008)zbMATHCrossRefGoogle Scholar
  22. 22.
    Cayton, L.: Algorithms for manifold learning. Technical Report CS2008-0923, University of California (2005)Google Scholar
  23. 23.
    Ihler, A.: Nonlinear Manifold Learning 6.454 Summary. MIT (2003)Google Scholar
  24. 24.
    Tenenbaum, J.B., de Silva, V., Langford, J.C.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290, 2319–2323 (2000)CrossRefGoogle Scholar
  25. 25.
    Saul, L.K., Roweis, S.T.: Think Globally, Fit Locally: Unsupervised Learning of low Dimensional Manifolds. Journal of Machine Learning Research 4, 119–155 (2003)MathSciNetGoogle Scholar
  26. 26.
    Iwaneczko, P., Jędrasiak, K., Daniec, K., Nawrat, A.: A prototype of unmanned aerial vehicle for image acquisition. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2012. LNCS, vol. 7594, pp. 87–94. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Henryk Josiński
    • 1
  • Daniel Kostrzewa
    • 1
  • Agnieszka Michalczuk
    • 1
  • Adam Świtoński
    • 1
  • Konrad Wojciechowski
    • 1
  1. 1.Institute of Computer ScienceSilesian University of TechnologyGliwicePoland

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