Skip to main content
SpringerLink
Log in

Discriminative face recognition via kernel sparse representation

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

Abstract

Sparse representation (SR) is a popular method in pattern recognition and computer vision, and achieves the noticeable performance for face recognition (FR) task. Nevertheless, the conventional SR algorithm is usually computationally expensive due to the solution of representation coefficients via l1-regularization minimization problem. Besides, the internal relationship of data, such as nonlinear structure is neglected by the classification procedure conducted on the original data space. To solve these problems, this paper proposes a discriminative FR method using kernel sparse representation (KSR) based on the framework of l2-regularization. With the goal of extracting richer information, a kernel function is used to map the original face samples into a high feature space. Then, a new SR method based on the framework of l2-regularization is designed to represent the face samples on this new space. This method can produce a discriminative representation for each face sample. In addition, the proposed method offers a computational efficient algorithm for FR task. Extensive experiments conducted on the face databases show the effectiveness of our method.

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

Similar content being viewed by others

References

  1. Aronszajn N (1950) Theory of reproducing kernels. Trans Am Math Soc 68(3):337–404

    Article  MathSciNet  Google Scholar 

  2. Bach FR, Jordan MI (2002) Kernel independent component analysis. J Mach Learn Res 3(1):1–48

    MathSciNet  MATH  Google Scholar 

  3. Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2(1):183–202

    Article  MathSciNet  Google Scholar 

  4. Bian X, Krim H, Bronstein A, Dai L (2016) Sparsity and nullity: paradigms for analysis dictionary learning. SIAM J Imaging Sci 9(3):1107–1126

    Article  MathSciNet  Google Scholar 

  5. Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167

    Article  Google Scholar 

  6. Chen S, Chen G, Gu R (2013) An efficient L2-norm regularized least-squares temporal difference learning algorithm. Knowl Based Syst 45(3):94–99

    Article  MathSciNet  Google Scholar 

  7. Gao S, Tsang IW, Chia LT (2013) Sparse representation with kernels. IEEE Trans Image Process 22(2):423–434

    Article  MathSciNet  Google Scholar 

  8. Goel N, Bebis G (2005) Face recognition experiments with random projection. Proc SPIE Int Soc Opt Eng 5776:426–437

    Google Scholar 

  9. Hofmann T, Schölkopf B, Smola AJ (2008) Kernel methods in machine learning. Ann Stat 36:1171–1220

    Article  MathSciNet  Google Scholar 

  10. Hu W, Li W, Zhang X, Maybank S (2015) Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation. IEEE Trans. on Pattern Analysis and Machine Intelligence 37(4):816–833

    Article  Google Scholar 

  11. Jing G, Shi Y, Kong D, Ding W, Yin B (2014) Image super-resolution based on multi-space sparse representation. Multimed Tools Appl 70(2):741–755

    Article  Google Scholar 

  12. Krim H, Hamza AB (2015) Geometric methods in signal and image analysis. Cambridge University press

  13. Liu Z, Pu J, Huang T, Qiu Y (2013) A novel classification method for palmprint recognition based on reconstruction error and normalized distance. Appl Intell 39(2):307–314

    Article  Google Scholar 

  14. Lyons M, Akamatsu S, Kamachi M, Gyoba J (1998) Coding facial expressions with gabor wavelets. Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on. IEEE, 1998: 200–205

  15. Martinez AM (1998) The AR face database. Cvc Technical Report 24

  16. Mika S, Ratsch G, Weston J, Scholkopf B, Mullers KR (1999) Fisher discriminant analysis with kernels. IEEE Int Workshop Neural Netw Sign Process IX:41–48

    Google Scholar 

  17. Naseem I, Togneri R, Bennamoun M (2010) Linear regression for face recognition. IEEE Trans Pattern Anal Mach Intell 32(11):2106–2112

    Article  Google Scholar 

  18. Saitoh S (1988) Theory of reproducing kernels and its applications (Vol. 189). Longman

  19. Samaria FS, Harter AC (1994) Parameterisation of a stochastic model for human face identification. In: Applications of Computer Vision, 1994. Proceedings of the Second IEEE Workshop on, 1994: 138–142

  20. Schölkopf B, Smola A, Müller KR (1997) Kernel principal component analysis. (1997) Int Conf Artif Neural Netw ICANN: 583–588

  21. Schölkopf B, Smola A, Müller KR (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299–1319

    Article  Google Scholar 

  22. Shi Q, Eriksson A, DHA Van, Shen C (2011) Is face recognition really a compressive sensing problem?. Comput Vision Pattern Recogn: 553–560

  23. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222

    Article  MathSciNet  Google Scholar 

  24. Wagner A, Wright J, Ganesh A, Zhou Z, Mobahi H, Ma Y (2012) Toward a practical face recognition system: robust alignment and illumination by sparse representation. IEEE Trans Pattern Anal Mach Intell 34(2):372–386

    Article  Google Scholar 

  25. Wang B, Li W, Poh N, Liao Q (2013) Kernel collaborative representation-based classifier for face recognition. 2013 IEEE Int Conf Acoust, Speech Sign Process (ICASSP) IEEE: 2877–2881

  26. Wang D, Lu H, Yang MH (2015) Kernel collaborative face recognition. Pattern Recogn 48(10):3025–3037

    Article  Google Scholar 

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

    Article  Google Scholar 

  28. Wright J, Ma Y, Mairal J, Sapiro G, Huang T, Yan S (2010) Sparse representation for computer vision and pattern recognition. Proc IEEE 98(6):1031–1044

    Article  Google Scholar 

  29. Xu Y, Zhang D, Yang J, Yang JY (2011) A two-phase test sample sparse representation method for use with face recognition. IEEE Trans Circuits Syst Video Technol 21(9):1255–1262

    Article  MathSciNet  Google Scholar 

  30. Xu Y, Zhu X, Li Z, Liu G, Lu Y, Liu H (2013) Using the original and ‘symmetrical face’ training samples to perform representation based two-step face recognition. Pattern Recogn 46(4):1151–1158

    Article  Google Scholar 

  31. Xu Y, Zhu Q, Fan Z, Zhang D, Mi J, Lai Z (2013) Using the idea of the sparse representation to perform coarse-to-fine face recognition. Inform Sciences 238(7):138–148

    Article  MathSciNet  Google Scholar 

  32. Xu Y, Zhu Q, Chen Y, Pan JS (2013) An improvement to the nearest neighbor classifier and face recognition experiments. IJICIC 9(2):543–554

    Google Scholar 

  33. Xu Y, Zhang B, Zhong Z (2015) Multiple representations and sparse representation for image classification. Pattern Recogn Lett 68:9–14

    Article  Google Scholar 

  34. Xu Y, Zhang Z, Lu G, Yang J (2016) Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification. Pattern Recogn 54(C):68–82

    Article  Google Scholar 

  35. Yang AY, Sastry SS, Ganesh A, Ma Y (2010) Fast ℓ 1 -minimization algorithms and an application in robust face recognition: A review. In: 2010 IEEE International Conference on Image Processing(ICIP). IEEE: 1849–1852

  36. Yang J, Zhang L, Xu Y, Yang JY (2012) Beyond sparsity: the role of L1 -optimizer in pattern classification. Pattern Recogn 45(3):1104–1118

    Article  Google Scholar 

  37. Yang M, Zhang L, Zhang D, Wang S (2012) Relaxed collaborative representation for pattern classification. 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE: 2224–2231

  38. Yang J, Chu D, Zhang L, Xu Y, Yang J (2013) Sparse representation classifier steered discriminative projection with applications to face recognition. IEEE Trans Neural Netw Learn Syst 24(7):1023–1035

    Article  Google Scholar 

  39. Yong X, Zhong Z, Jian Y, You J, Zhang D (2016) A new discriminative sparse representation method for robust face recognition via l2 regularization. IEEE Trans Neural Netw Learn Syst (99):1–10

  40. Zeng S, Yang X, Gou J (2017) Using kernel sparse representation to perform coarse-to-fine recognition of face images. Optik - Int J Light Electron Optics 140:528–535

    Article  Google Scholar 

  41. Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition? In: 2011 IEEE international conference on computer vision. IEEE, pp 471–478

  42. Zhang H, Zhang Y, Huang TS (2013) Pose-robust face recognition via sparse representation. Pattern Recogn 46(5):1511–1521

    Article  Google Scholar 

  43. Zhang X, Li W, Hu W, Ling H, Maybank S (2013) Block Covariance Based L1 Tracker with a Subtle Template Dictionary. Pattern Recogn 46(7):1750–1761

    Article  Google Scholar 

  44. Zhang X, Hu W, Chen S, Maybank S (2014) Graph-Embedding-Based Learning for Robust Object Tracking. IEEE Trans Ind Electron 61(2):1072–1084

    Article  Google Scholar 

  45. Zhang L, Zhou WD, Li FZ (2015) Kernel sparse representation-based classifier ensemble for face recognition. Multimed Tools Appl 74(1):123–137

    Article  Google Scholar 

  46. Zhang X, Hu W, Xie N, Bao H, Maybank S (2015) A Robust Tracking System for Low Frame Rate Video. Int J Comput Vis 115(3): 279–304

    Article  MathSciNet  Google Scholar 

  47. Zhang Z, Xu Y, Yang J, Li X, Zhang D (2017) A survey of sparse representation: algorithms and applications. IEEE Access 3:490–530

    Article  Google Scholar 

  48. Zhu P, Zhang L, Hu Q, Shiu SCK (2012) Multi-scale patch based collaborative representation for face recognition with margin distribution optimization. In European Conference on Computer Vision (ECCV): 822–835

  49. Zhu Q, Xu Y, Wang JH, Fan Z (2012) Kernel based sparse representation for face recognition. 2012 Int Conf Pattern Recogn (ICPR): 1703–1706

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No.61672333, 61402274, 61703096), the Pivot Science and Technology Innovation Team of Shaanxi Province of China (No.2014KTC-18), the Key Science and Technology Program of Shaanxi Province of China (No.2016GY-081), the Science Research and Development Program of Shaanxi Province (No.2016NY-176), Fundamental Research Funds for the Central Universities (No.GK201803059, GK201803088), China Postdoctoral Science Foundation(No.2017 M611655), Industry university cooperative education project of Higher Education Department of the Ministry of Education (No.201701023062) and Interdisciplinary Incubation Project of Learning Science of Shaanxi Normal University.

Author information

Authors and Affiliations

  1. Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an, 710062, China

    Keyou Zhang & Yali Peng

  2. Engineering Laboratory of Teaching Information Technology of Shaanxi Province, Xi’an, 710119, Shaanxi, China

    Keyou Zhang, Yali Peng & Shigang Liu

  3. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Keyou Zhang & Shigang Liu

Authors
  1. Keyou Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yali Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shigang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yali Peng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, K., Peng, Y. & Liu, S. Discriminative face recognition via kernel sparse representation. Multimed Tools Appl 77, 32243–32256 (2018). https://doi.org/10.1007/s11042-018-6110-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6110-6

Keywords

Search

Navigation