Skip to main content
Log in

Random-filtering based sparse representation parallel face recognition

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

Abstract

Collaborative representation classification (CRC) has attracted increasing attention in face recognition (FR) tasks. The two-phase sparse representation (TPSR) methods are the improved schemes. However, most existing TPSR methods decrease training samples in the first step, resulting in less similarities or discrimination for representation, even unstable classification. In this paper, we propose a novel two-phase representation based FR approach, called random-filtering based sparse representation (RFSR) scheme. In the first phase, to increase the similarity in the same class and the discrimination between different classes, RFSR uses original training samples and their corresponding random-filtering virtual samples to construct a new training set. In the second phase, it exploits the new training set to perform CRC. Furthermore, the time cost of RFSR becomes much more expensive, with the increasement of the scale of training set. To further save the computational time, the parallel measure of RFSR is proposed. The experiment results indicate that our RFSR method can improve the FR accuracy just using a simple way to obtain more training samples, along with a higher time efficiency.

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

Access this article

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Ding C, Xu C, Tao D (2015) Multi-task pose-invariant face recognition. IEEE Trans Image Process Publ IEEE Signal Process Soc 24(3):980–93

    Article  MathSciNet  Google Scholar 

  2. Feng Q, Yuan C, Huang J et al (2015) Center-based weighted kernel linear regression for image classification. In: Proceedings of IEEE International Conference on Image Processing, vol 2015, pp 3630–3634

  3. Feng Q, Yuan C, Pan JS et al (2017) Superimposed sparse parameter classifiers for face recognition. IEEE Trans Cybern PP(99):1–13

    Google Scholar 

  4. Georghiades A, Belhumeur PN, Kriegman DJ (1997) Yale face database, center for computational vision and control at Yale University, p 2

  5. Hsieh PC, Tung PC (2009) A novel hybrid approach based on sub-pattern technique and whitened PCA for face recognition. Pattern Recogn 42(5):978–984

    Article  Google Scholar 

  6. Huang SM, Yang JF (2012) Improved principal component regression for face recognition under illumination variations. IEEE Signal Process Lett 19(4):179–182

    Article  Google Scholar 

  7. Huang SM, Yang JF (2012) Kernel linear regression for low resolution face recognition under variable illumination. In: Proceedings of IEEE International Conference on Acoustics,speech and signal processing, vol 2012, pp 1945–1948

  8. Ji HK, Sun QS, Ji ZX et al (2016) Collaborative probabilistic labels for face recognition from single sample per person. Pattern Recogn 62(C):125–134

    Google Scholar 

  9. Lei Y, Guo Y, Hayat M et al (2016) A Two-Phase Weighted Collaborative Representation for 3D partial face recognition with single sample. Pattern Recogn 52 (C):218–237

    Article  Google Scholar 

  10. Leng L, Zhang J, Khan MK et al (2010) Dynamic weighted discrimination power analysis: a novel approach for face and palmprint recognition in DCT domain. Int J Phys Sci 5(17):2543–2554

    Google Scholar 

  11. Leng L, Zhang J, Xu J et al (2010) Dynamic weighted discrimination power analysis in DCT domain for face and palmprint recognition. In: Proceedings of IEEE International Conference on Information and Communication Technology Convergence (ICTC), vol 2010, pp 467–471

  12. Leng L, Zhang J, Chen G et al (2011) Two-directional two-dimensional random projection and its variations for face and palmprint recognition. In: Proceedings of International Conference on Computational Science and its Applications, vol 2011, pp 458–470

  13. Leng L, Zhang S, Bi X et al (2012) Two-dimensional cancelable biometric scheme. In: Proceedings of IEEE International Conference on Wavelet Analysis and Pattern Recognition, vol 2012, pp 164–169

  14. Liu BD, Gui L, Wang Y et al (2017) Class specific centralized dictionary learning for face recognition. Multimed Tools Appl 76(3):4159–4177

    Article  Google Scholar 

  15. Low CY, Teoh BJ, Ng CJ (2016) Multi-Fold gabor, PCA and ICA filter convolution descriptor for face recognition. IEEE Trans Circ Syst Video Technol PP (99):1–1

    Google Scholar 

  16. Lu J, Plataniotis KN, Venetsanopoulos AN (2003) Face recognition using LDA-based algorithms. IEEE Trans Neural Netw 14(1):195

    Article  Google Scholar 

  17. Martinez AM (1998) The AR face database. CVC technical report, pp 24

  18. Mika S, Rätsch G, Weston J et al (1999) Fisher discriminant analysis with kernels. In: proceedings of the 1999 IEEE Signal Processing Society Workshop, Neural Netw Signal Process Ix, vol 1999, pp 41–48

  19. 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 

  20. Naseem I, Togneri R, Bennamoun M (2012) Robust regression for face recognition. In: Proceedings of the International Conference on Pattern Recognition, vol 2012, pp 1156–1159

  21. Schölkopf B, Smola A, Müller KR (1997) Kernel principal component analysis. Lect Notes Comput Sci 1327(4):583–588

    Article  Google Scholar 

  22. Shen F, Yang W, Li H et al (2016) Robust regression based face recognition with fast outlier removal. Multimed Tools Appl 75(20):1–12

    Article  Google Scholar 

  23. Tang DY, Zhu NB, Yu F et al (2014) A novel sparse representation method based on virtual samples for face recognition. Neural Comput Appl 24(3-4):513–519

    Article  Google Scholar 

  24. Wagner A, Wright J, Ganesh A et al (2009) Towards a practical face recognition system: Robust registration and illumination by sparse representation. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol 2009, pp 597–604

  25. Wagner A, Wright J, Ganesh A et al (2012) Toward a practical face recognition system: robust alignment and illumination by sparse representation. IEEE Trans Pattern Anal Mach Intell 34(2):372

    Article  Google Scholar 

  26. Wolf L, Hassner T, Taigman Y (2009) Similarity scores based on background samples. In: Proceedings of Asian Conference on Computer Vision, vol 2009, pp 88–97

  27. Wright J, Yang AY, Ganesh A et al (2008) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  28. Xu Y, Zhang D, Yang J et al (2011) A Two-Phase test sample sparse representation method for use with face recognition. IEEE Trans Circ Syst Video Technol 21(9):1255–1262

    Article  MathSciNet  Google Scholar 

  29. Xu Y, Zhu Q (2013) A simple and fast representation-based face recognition method. Neural Comput Appl 22(7-8):1543–1549

    Article  Google Scholar 

  30. Xu Y, Li X, Yang J et al (2014) Integrating conventional and inverse representation for face recognition. IEEE Trans Cybern 44(10):1738–1746

    Article  Google Scholar 

  31. Xu Y, Zhong Z, Yang J et al (2016) A new discriminative sparse representation method for robust face recognition via l 2 regularization. IEEE Trans Neural Netw Learn Syst PP(99):1–10

    Article  Google Scholar 

  32. Yang M, Zhang L, Yang J et al (2013) Regularized robust coding for face recognition. IEEE Trans Image Process 22(5):1753

    Article  MathSciNet  Google Scholar 

  33. Yang J, Zhang D, Frangi AF et al (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131

    Article  Google Scholar 

  34. Ying T, Yang J, Zhang Y et al (2016) Face recognition with pose variations and misalignment via orthogonal procrustes regression. IEEE Trans Image Process Publ IEEE Signal Process Soc 25(6):2673

    MathSciNet  Google Scholar 

  35. Zhang L, Yang M, Feng X et al (2012) Collaborative representation based classification for face recognition. computer science

  36. Zhang G, Sun H, Ji Z et al (2016) Cost-sensitive dictionary learning for face recognition. Pattern Recogn 60(C):613–629

    Article  Google Scholar 

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

    Article  Google Scholar 

  38. Zhu NB, Li ST (2014) A Kernel-based sparse representation method for face recognition. Neural Comput Appl 24(3-4):845–852

    Article  Google Scholar 

  39. Zuo W, Zhang D, Yang J et al (2006) BDPCA plus LDA: a novel fast feature extraction technique for face recognition. IEEE Trans Syst Man Cybern Part B 36(4):946–53

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Siwang Zhou.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, D., Zhou, S. & Yang, W. Random-filtering based sparse representation parallel face recognition. Multimed Tools Appl 78, 1419–1439 (2019). https://doi.org/10.1007/s11042-018-6166-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6166-3

Keywords

Navigation