Advertisement

Gait Recognition Using Wavelet Descriptors and Independent Component Analysis

  • Jiwen Lu
  • Erhu Zhang
  • Cuining Jing
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

This paper proposes an approach to automatic gait recognition based on wavelet descriptors and independent component analysis (ICA) for the purpose of human identification at a distance. Firstly, the background extraction method is applied to subtract the moving human figures accurately and to obtain binary silhouettes. Secondly, these silhouettes are described with wavelet descriptors and converted into one-dimensional signals to get the independent components (ICs) of these feature signals through ICA. Then, a fast and robust fixed-point algorithm for calculating the ICs is adopted and a selection criterion how to choose ICs is given. Lastly, the nearest neighbor and support vector machine classifiers are chosen for recognition and the method is tested on the XAUT and NLPR gait database. Experimental results show that our method has encouraging recognition accuracy with comparatively low computational cost.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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, 1505–1518 (2003)CrossRefGoogle Scholar
  2. Mowbray, S.D., Nixon, M.S.: Automatic Gait Recognition via Fourier Descriptors of Deformable Objects. In: Proceedings of 4th International Conference on Audio and Video-based Biometrics Person Authentication (2003)Google Scholar
  3. Yu, S., Wang, L., Hu, W., Tan, T.: Gait Analysis for Human Identification in Frequency Domain. In: Proceedings of the Third International Conference on Image and Graphics, Hong Kong, China (2004)Google Scholar
  4. Han, H.: Study on Gait Extraction and Human Identification Based on Computer Vision. Doctoral thesis. University of Science and Technology Beijing, Beijing (2003) (in Chinese)Google Scholar
  5. Lu, J.W., Zhang, E.H., Zhang, Z.G., Xue, Y.X.: Gait Recognition Using Independent Component Analysis. In: Proceedings of 2nd International Symposium on Neural Networks, Chongqing, China (2005)Google Scholar
  6. Zhang, E.H., Lu, J.W., Duan, G.L.: Gait Recognition via Independent Component Analysis Based on Support Vector Machine and Neural Network. In: Proceedings of 1st International Conference on Natural Computation, Changsha, China (2005)Google Scholar
  7. Lee, L.: Gait Analysis for Classification. Doctoral Thesis. Massachusetts Institute of Technology, USA (2002)Google Scholar
  8. Wagg, D.K., Nixon, M.S.: An Automated Model-based Extraction and Analysis of Gait. In: IEEE Conference on Automatic Face and Gesture Recognition, Seoul, Korea, vol. 5, pp. 11–16 (2004)Google Scholar
  9. Hyvarinen, A.: Independent Component Analysis: Algorithm and Applications. Neural Networks 13, 411–430 (2000)CrossRefGoogle Scholar
  10. Hyvarinen, A.: A Fast and Robust Fixed-Point Algorithm for Independent Component Analysis. IEEE Transaction on Neural Networks 3, 626–634 (1999)CrossRefGoogle Scholar
  11. Yuen, P.C., Lai, J.H.: Face representation using independent component analysis. Pattern Recognition 35, 1247–1257 (2002)zbMATHCrossRefGoogle Scholar
  12. Deniz, O., Catrillon, M., Hernandez, M.: Face Recognition Using Independent Component Analysis and Support Vector Machines. Pattern Recognition Letters 24, 2153–2157 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jiwen Lu
    • 1
  • Erhu Zhang
    • 1
  • Cuining Jing
    • 1
  1. 1.Department of Information ScienceXi’an University of TechnologyXi’an, ShaanxiChina

Personalised recommendations