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Structure Sparsity for Multi-camera Gait Recognition

  • Qiyue Yin
  • Rong Sun
  • Liang Wang
  • Ran He
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)

Abstract

With the rapid development of surveillance technology, there are often several cameras in one scenario. The multi-camera usage to perform gait recognition becomes a challenge problem. This paper studies multi-camera gait recognition via structure sparsity. For the multi-camera structure in the training set, we propose a structure sparsity algorithm to learn informative and discriminative sparse representations; and for the structure in the testing set, we develop a new classification criteria based on the reconstruction error of learned sparse representations. In addition, we learn a dictionary from the original gait data to further improve recognition accuracy meanwhile reduce computational cost. Experimental results show that the proposed method can efficiently deal with the multi-camera gait recognition problem and outperforms the state-of-the-art sparse representation methods.

Keywords

multi-camera gait recognition structure sparsity sparse representation 

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References

  1. 1.
    Wang, L., Tan, T., Ning, H., Hu, W.: Silhouette Analysis-based Gait Recognition for Human Identification. TPAMI 25(12), 1505–1518 (2003)CrossRefGoogle Scholar
  2. 2.
    Zhang, R., Vogler, C., Metaxas, D.: Human Gait Recognition. In: CVPRW, 18 pages (2004)Google Scholar
  3. 3.
    Zhao, G., Liu, G., Li, H., Pietikainen, M.: 3D Gait Recognition using Multiple Cameras. In: FGR, pp. 529–534 (2006)Google Scholar
  4. 4.
    Han, J., Bhanu, B.: Individual Recognition Using Gait Energy Image. TPAMI 28(2), 316–322 (2006)CrossRefGoogle Scholar
  5. 5.
    Tao, D., Li, X., Wu, X., Maybank, S.J.: General Tensor Discriminant Analysis and Gabor Features for Gait Recognition. TPAMI 29(10), 1700–1715 (2007)CrossRefGoogle Scholar
  6. 6.
    Wang, C., Zhang, J., Wang, L., Pu, J., Yuan, X.: Human Identification Using Temporal Information Preserving Gait Template. TPAMI (99) (2011)Google Scholar
  7. 7.
    Lei, Z., Chu, R.F., He, R., Liao, S.C., Li, S.: Face Recognition by Discriminant Analysis with Gabor Tensor Representation. In: Advances in Biometrics (2007)Google Scholar
  8. 8.
    Kusakunniran, W., Wu, Q., Zhang, J., Li, H.: Support Vector Regression for Multi-view Gait Recognition based on Local Motion Feature Selection. In: CVPR, pp. 974–981 (2010)Google Scholar
  9. 9.
    Kusakunniran, W., Wu, Q., Zhang, J., Li, H.: Gait Recognition under Various Viewing Angles based on Correlated Motion Regression. TCSVT (99), 1–1 (2012)Google Scholar
  10. 10.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust Face Recognition via Sparse Representation. TPAMI 31(2), 210–227 (2009)CrossRefGoogle Scholar
  11. 11.
    Wright, J., Ma, Y., Mairal, J., Sapiro, G., Huang, T.S., Yan, S.: Sparse Representation for Computer Vision and Pattern Recognition. Proceedings of the IEEE 98(6), 1031–1044 (2010)CrossRefGoogle Scholar
  12. 12.
    He, R., Zheng, W.-S., Hu, B.-G., Kong, X.W.: A Regularized Correntropy Framework for Robust Pattern Recognition. Neural Computation 23(8), 2074–2100 (2011)zbMATHCrossRefGoogle Scholar
  13. 13.
    He, R., Hu, B.-G., Zheng, W.-S., Kong, X.W.: Robust Principal Component Analysis Based on Maximum Correntropy Criterion. IEEE Transactions on Image Processing 20(6), 1485–1494 (2011)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Yang, Q., Xue, D.-Y., Cui, J.-J.: Gait Recognition Using Sparse Representation. Journal of Northeastern University(Natural Science), 136–139 (2012)Google Scholar
  15. 15.
    Gong, M., Xu, Y., Yang, X., Zhang, W.: Gait Identification by Sparse Representation. FSKD 3, 1719–1723 (2011)Google Scholar
  16. 16.
    Nie, F., Huang, H., Cai, X., Ding, C.: Efficient and Robust Feature Selection via Joint l 2,1 − Norms Minimization. In: NIPS, vol. 23, pp. 1813–1821 (2010)Google Scholar
  17. 17.
    He, R., Sun, Z.N., Tan, T.N., Zheng, W.S.: Recovery of Corrupted Low-Rank Matrices via Half-Quadratic based Nonconvex Minimization. In: IEEE CVPR (2011)Google Scholar
  18. 18.
    He, R., Tan, T.N., Wang, L., Zheng, W.S.: L21 Regularized Correntropy for Robust Feature Selection. In: IEEE CVPR (2012)Google Scholar
  19. 19.
    Zheng, S., Huang, K.Q., Tan, T.N., He, R., Zhang, J.: Robust View Transformation Model for Gait Recognition. In: ICIP (2011)Google Scholar
  20. 20.
    Kusakunniran, W., Wu, Q., Li, H.D., He, R., Zhang, J.: Multiple Views Gait Recognition Using View Transformation Model Based on Optimized Gait Energy Image. In: IEEE ICCV (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Qiyue Yin
    • 1
  • Rong Sun
    • 1
  • Liang Wang
    • 2
  • Ran He
    • 2
  1. 1.Harbin Engineering UniversityChina
  2. 2.Institute of AutomationChinese Academy of SciencesChina

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