Skip to main content
Log in

Face Recognition Using A Low Rank Representation Based Projections Method

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

In this paper, a low rank representation based projections (LRRP) method is presented for face recognition. In LRRP, low rank representation is used to construct a nuclear graph to characterize the local compactness information by designing the local scatter matrix like SPP; the total separability information is characterized by the total scatter like PCA. LRRP seeks the projection matrix simultaneously maximizing the total separability and the local compactness. Experimental results on FERET, AR, Yale face databases and the PolyU finger-knuckle-print database demonstrate that LRRP works well for face recognition.

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

Similar content being viewed by others

References

  1. Zhao W, Chellappa R, Phillips PJ et al (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–459

    Article  Google Scholar 

  2. Bowyer KW, Chang K, Flym P (2006) A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition. Comput Vis Image Underst 101(1):1–15

    Article  Google Scholar 

  3. Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edn. Wiley, New York

    MATH  Google Scholar 

  4. Raudys SJ, Jain AK (1991) Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans Pattern Anal Mach Intell 13(3):252–264

    Article  Google Scholar 

  5. Swets DL, Weng J (1996) Using discriminant eigenfeatures for image retrieval. IEEE Trans Pattern Anal Mach Intell 18(8):831–836

    Article  Google Scholar 

  6. Belhumeur V, Hespanha J, Kriegman D (1997) Eigenfaces vs fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  7. Yang J, Yang J (2003) Why can LDA be performed in PCA transformed space? Pattern Recognit 36(2):563–566

    Article  Google Scholar 

  8. Hong Z, Yang J (1991) Optimal discriminant plane for a small number of samples and design method of classifier on the plane. Pattern Recognit 24(4):317–324

    Article  MathSciNet  Google Scholar 

  9. Yang J, Li H (2010) PCA based sequential feature space learning for gene selection. In: ICMLC2010, pp 3079–3084

  10. Hastie T, Tibshirani R (1995) Penalized discriminant analysis. Ann Stat 23:73–102

    Article  MathSciNet  MATH  Google Scholar 

  11. Chen LF, Liao HYM, Ko MT, Yu GJ (2000) A new lda-based face recognition system which can solve the small sample size problem. Pattern Recognit 33(1):1713–1726

    Article  Google Scholar 

  12. Yu H, Yang J (2001) A direct LDA algorithm for high dimensional data—with application to face recognition. Pattern Recognit 34(10):2067–2070

    Article  MATH  Google Scholar 

  13. Pang Y, Tao D, Yuan Y, Li X (2008) Binary two-dimensional PCA. IEEE Trans Syst Man Cybern B Cybern 38(4):1176–1180

    Article  Google Scholar 

  14. Zhuang X, Dai D (2005) Inverse fisher discriminant criteria for small sample size problem and its application to face recognition. Pattern Recognit 38(11):2192–2194

    Article  Google Scholar 

  15. Jin Z, Yang J, Hu Z, Lou Z (2001) Face recognition based on the uncorrelated discrimination transformation. Pattern Recognit 34(7):1405–1416

    Article  MATH  Google Scholar 

  16. Li X, Lin S, Yan S, Xu D (2008) Discriminant locally linear embedding with high-order tensor data. IEEE Trans Syst Man Cybern B Cybern 38(2):342–352

    Article  Google Scholar 

  17. Loog M (2001) Approximate pairwise accuracy criterion for multiclass linear dimension reduction: generalization of the fisher criterion, IEEE Trans. Pattern Anal Mach Intell 26(7):762–766

    Article  Google Scholar 

  18. Xu J, Yang J (2013) K-local hyperplane distance nearest neighbor classifier oriented local discriminant analysis. Inf Sci 232(2):11–26

    Article  MathSciNet  MATH  Google Scholar 

  19. Lu G, Wang Y (2012) Feature extraction using a fast null space based linear discriminant analysis algorithm. Inf Sci 193(15):72–80

    Article  MathSciNet  Google Scholar 

  20. Tao D, Li X, Wu X, Maybank SJ (2009) Geometric mean for subspace selection. IEEE Trans Pattern Anal Mach Intell 31(2):260–274

    Article  Google Scholar 

  21. Xu Y, Zhang D, Yang J, Yang J-Y (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  Google Scholar 

  22. Yang W, Wang J, Ren M, Yang J (2009) Feature extraction based on Laplacian bidirectional maximum margin criterion. Pattern Recognit 42(11):2327–2334

    Article  MATH  Google Scholar 

  23. Yang W, Wang J, Ren M, Zhang L, Yang J (2009) Feature extraction using fuzzy inverse FDA. Neurocomputing 72(13–15):3384–3390

    Article  Google Scholar 

  24. Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290:2319–2323

    Article  Google Scholar 

  25. Roweis ST, Saul LK (2000) Nonlinear dimension reduction by locally linear embedding. Science 290:2323–2326

    Article  Google Scholar 

  26. Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396

    Article  MATH  Google Scholar 

  27. Donoho David L, Grimes Carrie (2003) Hessian eigenmaps: locally linear embedding techniques for high-dimensional data. Proc Natl Acad Sci 100(10):5591–5596

    Article  MathSciNet  MATH  Google Scholar 

  28. Liu W, Tao D (2013) Multiview Hessian regularization for image annotation. IEEE Trans Image Process 22(7):2676–2687

    Article  MathSciNet  Google Scholar 

  29. He X, Yan S, Hu Y, Niyogi P, Zhang H (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Article  Google Scholar 

  30. Yang J, Zhang D, Yang J, Niu B (2007) Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Trans Pattern Anal Mach Intell 29(4):650–664

    Article  Google Scholar 

  31. Xu Y, Zhong A, Yang J, Zhang D (2010) LPP solution schemes for use with face recognition. Pattern Recognit 43(12):4165–4176

    Article  MATH  Google Scholar 

  32. Yan S, Xu D, Zhang B, Zhang H (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51

    Article  Google Scholar 

  33. Wang H, Che S, Hu Z, Zheng W (2008) Locaility-preserved maximum information projection. IEEE Trans Neural Netw 19(4):571–585

    Article  Google Scholar 

  34. Zhou D, Huang J, Schölkopf B (2006) Learning with hypergraphs: clustering, classification, and embedding. In: Proceedings on neural information processing systems, Vancouver, BC, Canada, pp 1601–1608

  35. Yu J, Tao D, Wang M (2012) Adaptive hypergraph learning and its application in image classification. IEEE Trans Image Process 21(7):3262–3272

    Article  MathSciNet  Google Scholar 

  36. Yu J, Rui R, Tang Y, Tao D (2014) High-order distance based multiview stochastic learning in image classification. IEEE Trans Cybern 44(12):2431–2442

    Article  Google Scholar 

  37. Yu J, Rui R, Tao D (2014) Click prediction for web image reranking using multimodal sparse coding. IEEE Trans Image Process 23(5):2019–2032

    Article  MathSciNet  Google Scholar 

  38. Yang J, Wright J, Huang T, Ma Y (2008) Image super-resolution as sparse representation of raw patches. In: CVPR2008

  39. Rao S, Tron R, Vidal R, Ma Y (2008) Motion segmentation via robust subspace separation in the presence of outlying, incomplete, and corrupted trajectories. In: CVPR2008

  40. Mairal J, Sapiro G, Elad M (2008) Learning multiscale sparse representations for image and video restoration. SIAM MMS 7(1):214–241

    Article  MathSciNet  MATH  Google Scholar 

  41. Liu W, Tao D, Cheng J, Tang Y (2014) Multiview Hessian discriminative sparse coding for image annotation. Comput Vision Image Underst 118:50–60

    Article  Google Scholar 

  42. Liu W, Song C, Wang Y (2012) Facial expression recognition based on discriminative dictionary learning. In: ICPR2012

  43. Wang H, Yuan C, Hu W et al (2014) Action recognition using nonnegative action component representation and sparse basis selection. IEEE Trans Image Process 23(2):570–581

    Article  MathSciNet  Google Scholar 

  44. Yu J, Hong R, Wang M, You J (2014) Image clustering based on sparse patch alignment framework. Pattern Recognit 47:3512–3519

    Article  Google Scholar 

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

    Article  Google Scholar 

  46. Qiao L, Chen S, Tan X (2010) Sparsity preserving projections with applications to face recognition. Pattern Recognit 43(1):331–341

    Article  MATH  Google Scholar 

  47. Cheng B, Yang J, Yan S, Fu Y, Huang T (2010) Learning with L1-graph for image analysis. IEEE Trans Image Process 19(4):858–866

    Article  MathSciNet  Google Scholar 

  48. Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition. In: ICCV2011

  49. Liu G, Lin Z, Yan S, Su J, Yu Y, Ma Y (2013) Robust recovery of subspace structures by low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(1):171–184

    Article  Google Scholar 

  50. Zhang N, Yang J (2013) Low-rank representation based discriminative projection for robust feature extraction. Neurocomputing 111:13–20

    Article  Google Scholar 

  51. Lin Z, Chen M, Wu L, Ma Y (2009) The argumented lagrange multiplier method for exact recovery of corrupted low-rank matrices. UIUC technical report UIUC-ENG-0902215, Tech. Rep

  52. Zhao D, Lin Z, Tang X (2007) Laplacian PCA and its applications. In: ICCV2007

  53. He X, Cai D, Yan S, Zhang H (2005) Neighborhood preserving embedding. In: ICCV2005, pp 1208–1213

  54. Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The FERET evaluation methodology for face recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22(10):1090–1104

    Article  Google Scholar 

  55. Phillips PJ (2004) The facial recognition technology (FERET) database, http://www.itl.nist.gov/iad/humanid/feret/feret_master.html

  56. Martinez AM, Benavente R (1998) The AR face database http://cobweb.ecn.purdue.edu/~aleix/aleix_face_DB.html

  57. Martinez AM, Benavente R (1998) The AR face database. CVC technical report #24, June 1998

  58. Zhang L, Zhang L, Zhang D, Zhu H (2010) Online finger-knuckle-print verification for personal authentication. Pattern Recognit 43(7):2560–2571

    Article  MATH  Google Scholar 

  59. The Hong Kong PolyU FKP database. http://www4.comp.polyu.edu.hk/~biometrics/FKP.htm

Download references

Acknowledgments

This project is partly supported by NSF of China (61375001), partly supported by the open fund of Key Laboratory of Measurement and partly supported by Control of Complex Systems of Engineering, Ministry of Education (No. MCCSE2013B01), and the Open Project Program of Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University (No. CDLS-2014-04). Understanding for Social Safety (Nanjing University of Science and Technology), (No. 30920130122006).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wankou Yang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Yang, W. & Shen, F. Face Recognition Using A Low Rank Representation Based Projections Method. Neural Process Lett 43, 823–835 (2016). https://doi.org/10.1007/s11063-015-9448-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-015-9448-z

Keywords

Navigation