Recognizing Gait Using Haar Wavelet and Support Vector Machine

Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 158)


This paper presented a new gait identification method based on Haar wavelet and Support Vector Machine. It solves the problem how to select key points and employ features to classify gaits. Firstly, images from video sequences are converted into binary silhouette. Haar wavelet transform is employed to obtain key points for distinct features, and the key points are analyzed. A subimage is utilized to represent gait features in each image, and employ Principal Component Analysis to reduce its dimensionalities. Finally, Support Vector Machine is employ to train and test, and it is helpful in analyzing features. Consequently, we can not only simplify the process, but also improve the recognition accuracy.


feature extraction gait recognition Haar wavelet Support Vector Machine 


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  1. 1.
    Boulgouris, N.V., Hatzinakos, D., Plataniotis, K.N.: Gait recognition: a challenging signal processing technology for biometric identification. IEEE Signal Process. Mag. 22(6), 78–90 (2005)CrossRefGoogle Scholar
  2. 2.
    Liu, Y., Collins, R., Tsin, Y.H.: Gait Sequence Analysis Using Frieze Patterns. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 657–671. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Kale, A., Cuntoor, N., Yegnanarayana, B., Rajagopalan, A.N., Chellappa, R.: Gait analysis for human identification. In: Proc. 4th Int. Conf. Audio- and Video-Based Person Authentication, Guilford, U.K., pp. 706–714 (2003)Google Scholar
  4. 4.
    Boulgouris, N.V., Plataniotis, K.N., Hatzinakos, D.: An angular transform of gait sequences for gait assisted recognition. In: Proc. IEEE Int. Conf. Image Processing, Singapore, pp. 857–860 (2004)Google Scholar
  5. 5.
    Zhang, H., Liu, Z., Zhao, H.: Automated Classification of Two Persons’ Interactive Activities. Journal of Software 5(8), 810–817 (2010)Google Scholar
  6. 6.
    Zhang, H., Liu, Z., Zhao, H., Cheng, G.: Recognizing Human Activities by Key Frame in Video Sequences. Journal of Software 5(8), 818–825 (2010)Google Scholar
  7. 7.
    Zhang, H., Liu, Z., Zhao, H.: Human Activities for Classification via Feature Points. Information Technology Journal 10(5), 974–982 (2010)MathSciNetGoogle Scholar
  8. 8.
    Alfred, H.: Zur Theorie der orthogonalen Funktionensysteme. Mathematische Annalen 69(3), 331–371 (1910)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Cortes, C., Vapnik, V.: Support Vector Networks. Machine Learning 20, 273–297 (1995)MATHGoogle Scholar
  10. 10.
    Liu, Z., Sarkar, S.: Improved Gait Recognition by Gait Dynamics Normalization. IEEE Trans. Pattern Anal. Mach. Intell. 28(6), 863–876 (2006)CrossRefGoogle Scholar
  11. 11.
    Chen, S., Tian, Y., Huang, W., et al.: Automatic human gait recognition using temporal template. Journal of Xidian University 34(4), 605–610 (2007)Google Scholar
  12. 12.
    Ye, B., Wen, Y.: Gait recognition based on DWT and SVM. Journal of Image and Graphics 12(6), 1055–1063 (2007)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institute of Oceanographic InstrumentationShandong Academy of SciencesQingdaoP.R. China
  2. 2.Information Development CenterXi’an Dongfeng Instrument FactoryXi’anP.R. China

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