Skip to main content
SpringerLink
Log in

Class specific centralized dictionary learning for face recognition

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Sparse representation based classification (SRC) and collaborative representation based classification (CRC) have demonstrated impressive performance for visual recognition. SRC and CRC assume that the training samples in each class contribute equally to the dictionary and thus generate the dictionary that consists of the training samples in the corresponding class. This may lead to high residual error and instability, to the detriment of recognition performance. One solution is to use the class specific dictionary learning (CSDL) algorithm, which has greatly improved the classification accuracy. However, the CSDL algorithm fails to consider the constraints to sparse codes. In particular, it cannot guarantee that the sparse codes in the same class will be concentrated based on the learned dictionary for each class. Such concentration is actually beneficial to classification. To address these limitations, in this paper, we propose a class specific centralized dictionary learning (CSCDL) algorithm to simultaneously consider the desired characteristics for both dictionary and sparse codes. The blockwise coordinate descent algorithm and Lagrange multipliers are used to optimize the corresponding objective function. Extensive experimental results on face recognition benchmark datasets demonstrate the superior performance of our CSCDL algorithm compared with conventional approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Belhumeur PN, Hespanha JP, Kriegman D (1997) Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intel 19(7):711–720

    Article  Google Scholar 

  2. Bertsekas DP (1999) Nonlinear programming. Athena Scientific Belmont, MA

    MATH  Google Scholar 

  3. Castrodad A, Sapiro G (2012) Sparse modeling of human actions from motion imagery. Int J Comput Vis 100(1):1–15

    Article  Google Scholar 

  4. Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2(3):27

    Google Scholar 

  5. Duda RO, Hart PE, Stork DG (2001) Pattern classification. A Wiley-Interscience:373–378

  6. Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process 15(12):3736–3745

    Article  MathSciNet  Google Scholar 

  7. Gao S, Tsang IW-H, Yi M (2014) Learning category-specific dictionary and shared dictionary for fine-grained image categorization. IEEE Trans Image Process 23 (2):623–634

    Article  MathSciNet  Google Scholar 

  8. Georghiades AS, Belhumeur PN, Kriegman DJ (2001) From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6):643–660

    Article  Google Scholar 

  9. He X, Yan S, Yuxiao H, Niyogi P, Zhang H-J (2005) Face recognition using laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(3):328–340

    Article  Google Scholar 

  10. Ho J, Yang MH, Lim J, Lee KC, Kriegman D (2003) Clustering appearances of objects under varying illumination conditions. In: Proceedings of 16th CVPR, vol 1, pp 1–11

  11. Lee H, Battle A, Raina R, Ng A (2006) Efficient sparse coding algorithms. In: Proceedings of Advances in neural information processing systems, pp 801–808

  12. Liu B-D, Shen B, Li X (2015) Locality sensitive dictionary learning for image classification. In: Proceedings of the 22nd ICIP

  13. Liu B-D, Shen B, Wang Y-X (2014) Class specific dictionary learning for face recognition. In: Proceedings of ICSPAC, pp 229–234

  14. Liu B-D, Wang Y-X, Shen B, Zhang Y-J, Hebert M (2014) Self-explanatory sparse representation for image classification. In: Proceedings of the 13th ECCV 2014, pp 600–616

  15. Liu B-D, Wang Y-X, Shen B, Zhang Y-J, Wang Y-J (2014) Blockwise coordinate descent schemes for sparse representation. In: Proceedings of the 39th ICASSP, pp 5304–5308

  16. Liu B-D, Wang Y-X, Zhang Y-J, Shen B (2013) Learning dictionary on manifolds for image classification. Pattern Recogn 46(7):1879–1890

    Article  Google Scholar 

  17. Liu B-D, Wang Y-X, Zhang Y-J, Zheng Y (2012) Discriminant sparse coding for image classification. In: Proceedings of 37th ICASSP, pp 2193–2196

  18. Mairal J, Ponce J, Sapiro G, Zisserman A, Bach FR (2009) Supervised dictionary learning. In: Proceedings of advances in neural information processing systems. MIT Press, pp 1033–1040

  19. Martinez AM (1998) The ar face database. CVC Technical Report:24

  20. Sim T, Baker S, Bsat M (2002) The cmu pose, illumination, and expression (pie) database. In: Proceedings of the 5th international conference on automatic face and gesture recognition , pp 46–51

  21. Sprechmann P, Sapiro G (2010) Dictionary learning and sparse coding for unsupervised clustering. In: Proceedings of the 35th ICASSP, pp 2042–2045

  22. Tao D, Li X, Xindong W, Maybank SJ (2009) Geometric mean for subspace selection. IEEE Trans Pattern Anal Mach Intel 31(2):260–274

    Article  Google Scholar 

  23. Tseng P (2001) Convergence of a block coordinate descent method for nondifferentiable minimization. J Optimiz Theory App 109(3):475–494

    Article  MathSciNet  MATH  Google Scholar 

  24. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3 (1):71–86

    Article  Google Scholar 

  25. Wang C, Yan S, Zhang L, Zhang H (2009) Multi-label sparse coding for automatic image annotation. In: Proceedings of the 22nd CVPR, pp 1643–1650

  26. Wang H, Yuan C, Weiming H, Sun C (2012) Supervised class-specific dictionary learning for sparse modeling in action recognition. Pattern Recognition 45 (11):3902–3911

    Article  Google Scholar 

  27. Wang Y-X, Gui L-Y, Zhang Y-J (2012) Neighborhood preserving non-negative tensor factorization for image representation. In: Proceedings of the 37th ICASSP, pp 3389–3392

  28. Wang Y-X, Hebert M (2015) Model recommendation: generating object detectors from few samples. In: Proceedings of the 28th CVPR, pp 1619–1628

  29. Wang Y-X, Hebert M (2016) Learned by transferring from unsupervised universal sources. In: Proceedings of the 30th AAAI

  30. Wang Y-X, Zhang Y-J (2011) Image inpainting via weighted sparse non-negative matrix factorization. In: Proceedings of the 18th ICIP, pp 3409–3412

  31. Wang Y-X, Zhang Y-J (2013) Nonnegative matrix factorization: a comprehensive review. IEEE Trans Knowl Data Eng 25(6):1336–1353

    Article  Google Scholar 

  32. Wright J, Yang AY, Ganesh A, Sastry S, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intel 31 (2):210–227

    Article  Google Scholar 

  33. Yan Y, Wang H, Suter D (2014) Multi-subregion based correlation filter bank for robust face recognition. Pattern Recognition 47(11):3487–3501

    Article  Google Scholar 

  34. Yang M, Zhang L, Feng X, Zhang D (2014) Sparse representation based fisher discrimination dictionary learning for image classification. Int J Comput Vis 109 (3):209–232

    Article  MathSciNet  MATH  Google Scholar 

  35. Yang M, Zhang L, Yang J, Zhang D (2010) Metaface learning for sparse representation based face recognition. In: Proceedings of the 17th ICIP, pp 1601–1604

  36. Zhang D, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition?. In: Proceedings of the 13th ICCV, pp 471–478

  37. Zhang Q, Li B (2010) Discriminative k-svd for dictionary learning in face recognition. In: Proceedings of 23rd CVPR, pp 2691–2698

  38. Zhao W, Chellappa R, Phillips JP, Rosenfeld A (2003) Face recognition: A literature survey. Acm Computing Surveys (CSUR) 35(4):399–458

    Article  Google Scholar 

  39. Zhou N, Yi S, Jinye P, Fan J (2012) Learning inter-related visual dictionary for object recognition. In: Proceedings of 25th CVPR, pp 3490–3497

Download references

Acknowledgment

This paper is supported partly by the National Natural Science Foundation of China (Grant No. 61402535, No. 61271407), the Natural Science Foundation for Youths of Shandong Province, China (Grant No. ZR2014FQ001) , Qingdao Science and Technology Project (No. 14-2-4-111-jch), and the Fundamental Research Funds for the Central Universities, China University of Petroleum (East China) (Grant No. 14CX02169A).

Author information

Authors and Affiliations

  1. College of Information and Control Engineering, China University of Petroleum, Qingdao, 266580, China

    Bao-Di Liu & Yan-Jiang Wang

  2. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Liangke Gui & Yu-Xiong Wang

  3. Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA

    Bin Shen

  4. Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China

    Xue Li

  5. Department of Informatics, Karlsruhe Institute of Technology, Karlsruhe, 76131, Germany

    Yuting Wang

Authors
  1. Bao-Di Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liangke Gui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuting Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu-Xiong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bin Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xue Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yan-Jiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bao-Di Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, BD., Gui, L., Wang, Y. et al. Class specific centralized dictionary learning for face recognition. Multimed Tools Appl 76, 4159–4177 (2017). https://doi.org/10.1007/s11042-015-3042-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-3042-2

Keywords

Search

Navigation