Human Gait Recognition Using Gait Flow Image and Extension Neural Network

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 380)

Abstract

This paper represents a new technique to recognize human gait using gait flow image (GFI) and extension neural network (ENN). GFI is a gait period-based technique, based on optical flow. ENN combines the extension theory and neural networks. So a novel ENN-based gait recognition method is proposed, which outperforms all existing methods. All the study is done on, CASIA-A database, which includes 20 persons. The results derived using ENN are compared with support vector machines (SVM) and nearest neighbor (NN) classifiers. ENN proved to have 98 % accuracy and lesser iterations as compared to other traditional methods.

Keywords

Gait flow image Extension neural network Optical flow Support vector machine Nearest neighbor 

References

  1. 1.
    Yu, C.C., Cheng, C.H., Fan, K.C.: A gait classification system using optical flow features. J. Inf. Sci. Eng. 30(1), 179–193 (2014)MATHGoogle Scholar
  2. 2.
    Yam, C., Nixon, M.S., Carter, J.N.: Automated person recognition by walking and running via model-based approaches. Pattern Recogn. 37(5), 1057–1072 (2004)CrossRefGoogle Scholar
  3. 3.
    Wang, L., Ning, H., Tan, T., Hu, W.: Fusion of static and dynamic body biometrics for gait recognition. IEEE Trans. Circuits Syst. Video Technol. 14(2), 149–158 (2004)CrossRefGoogle Scholar
  4. 4.
    Kale, A., Sundaresan, A., Rajagopalan, A.N., Cuntoor, N.P., Roy-Chowdhury, A.K., Kruger, V., Chellappa, R.: Identification of humans using gait. IEEE Trans. Image Process. 13(9), 1163–1173 (2004)CrossRefGoogle Scholar
  5. 5.
    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 Trans. Pattern Anal. Mach. Intell. 27(2), 162–177 (2005)CrossRefGoogle Scholar
  6. 6.
    Arora, P., Hanmandlu, M., Srivastava, S.: Gait based authentication using gait information image features. Pattern Recognition Letters (2015)Google Scholar
  7. 7.
    Arora, P., Srivastava, S.: Gait recognition using gait Gaussian image. In: IEEE Second International Conference on Signal Processing and Integrated Networks (SPIN), pp. 915–918. IEEE press (2015)Google Scholar
  8. 8.
    Lam, T.H., Cheung, K.H., Liu, J.N.: Gait flow image: A silhouette-based gait representation for human identification. Pattern Recogn. 44(4), 973–987 (2011)MATHCrossRefGoogle Scholar
  9. 9.
    Wang, L., Tan, T., Ning, H., Hu, W.: Silhoutte analysis based gait recognition for human identification. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 25(12), 1505–1518 (2003)CrossRefGoogle Scholar
  10. 10.
    Wang, M.H., Hung, C.P.: Extension neural network and its applications. Neural Netw. 16(5), 779–784 (2003)CrossRefGoogle Scholar
  11. 11.
    Horn, B.K., Schunck, B.G: Determining optical flow. In: Technical Symposium East, pp. 319–331. International Society for Optics and Photonics (1981)Google Scholar
  12. 12.
    Vapnik, V.N.: Estimation of dependences based on empirical data, vol. 41. Springer, New York (1982)Google Scholar
  13. 13.
    Wang, L., Tan, T., Hu, W., Ning, H.: Automatic gait recognition based on statistical shape analysis. IEEE Trans. Image Process. 12(9), 1120–1131 (2003)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Chen, S., Gao, Y.: An invariant appearance model for gait recognition. In: IEEE International Conference on Multimedia and Expo, pp. 1375–1378. IEEE press (2007)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Netaji Subhas Institute of TechnologyNew DelhiIndia

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