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Discriminative Weighted Low-Rank Collaborative Representation Classifier for Robust Face Recognition

  • Xielian Hou
  • Caikou Chen
  • Shengwei Zhou
  • Jingshan Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)

Abstract

Recently, low-rank collaborative representation classification (LCRC) has proven to have good performance under controlled conditions. However, this algorithm stipulates that each singular value of the kernel norm is equal, which limits its ability and flexibility to deal with practical problems. Moreover, training samples and test samples may be damaged due to occlusion or disguise; this factor may reduce the face recognition rate. This paper presents a novel robust face recognition based on discriminative weighted low-rank collaborative representation (WDLCRC). Based on the LCRC, we add the constraint of structural inconsistency and assign the singular values with different weights by adaptively weighting the kernel norm. It is proved through experiments that the recognition rate of WDLCRC on AR database and CMU PIE database is higher than that of SRC, CRC and LCRC algorithms.

Keywords

Face recognition Low-rank collaborative representation Inconsistent structure Adaptive weighting 

References

  1. 1.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse Representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)CrossRefGoogle Scholar
  2. 2.
    Zhang, L., Yang, M., Feng, X.: Spare representation or collaborative representation: which helps face recognition? In: Proceedings of International Conference on Computer Vision, Barcelona, Spain, pp. 471–478 (2011)Google Scholar
  3. 3.
    Lu, T., Guan, Y., Chen, D., Xiong, Z., He, W.: Low-rank constrained collaborative representation for robust face recognition. IEEE (2017)Google Scholar
  4. 4.
    Wei, C.-P., Chen, C.-F., Fank Wang, Y.-C.: Robust face recognition with structurally incoherent low-rank matrix decomposition. In: IEEE Transactions On Image Processing, vol. 23, no. 8, August 2014Google Scholar
  5. 5.
    Lin, Z., Liu, R., Su, Z.: Linearized alternating direction method with adaptive penalty for low-rank representation. In: Proceedings of Neural Information Processing Systems, Granada, Spain, pp. 1–9 (2011)Google Scholar
  6. 6.
    Gu, S., Xie, Q., Meng, D., Zuo, W., Feng, X., Zhang, L.: Weighted nuclear norm miniminzation and its applications to low level vision. Int. J. Comput. Vis. 1–26 (2016)Google Scholar
  7. 7.
    Cand’es, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM 58(3), 11 (2011)MathSciNetMATHGoogle Scholar
  8. 8.
    Ramirez, P.S., Sapiro, G.: Classification and clustering via dictionary learning with structured incoherence and shared features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3501–3508, June 2010Google Scholar
  9. 9.
    Yang, J., Zhang, Y.: Alternating direction algorithms for l1-problems in compressive sensing. SIAM J. Sci. Comput. 33(1), 250–278 (2011)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Martinez,A., Benavente, R.: The AR face database. CVC Technical Report, vol. 24 (1998)Google Scholar
  11. 11.
    Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-PIE. Image Vis. Comput. 28(5), 807–813 (2010)CrossRefGoogle Scholar
  12. 12.
    Chan, T.H., Jia, K., Gao, S., Lu, J., Zeng, Z., Ma, Y.: Pcanet: a simple deep learning baseline for image classification? IEEE Trans. Image Process. 24(12), 5017–5032 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Information EngineeringYangzhou UniversityYangzhouChina

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