Skip to main content
SpringerLink
Log in

Statistical binary patterns and post-competitive representation for pattern recognition

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

During the last decade, sparse representations have been successfully applied to design high-performing classification algorithms such as the classical sparse representation based classification (SRC) algorithm. More recently, collaborative representation based classification (CRC) has emerged as a very powerful approach, especially for face recognition. CRC takes advantage of SRC through the notion of collaborative representation, relying on the observation that the collaborative property is more crucial for classification than the l 1-norm sparsity constraint on coding coefficients used in SRC. This paper follows the same general philosophy of CRC and its main novelty is the application of a virtual collaborative projection (VCP) routine designed to train images of every class against the other classes to improve fidelity before the projection of the query image. We combine this routine with a method of local feature extraction based on high-order statistical moments to further improve the representation. We demonstrate using extensive experiments of face recognition and classification that our approach performs very competitively with respect to state-of-the-art classification methods. For instance, using the AR face dataset, our method reaches 100% of accuracy for dimensionality 300.

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
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Borgi MA, Labate D, El’arbi M, Amar CB (2015) Sparse multi-stage regularized feature learning for robust face recognition. Expert Syst Appl 42(1):269–279

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Theodorakopoulos I, Rigas I, Economou G, Fotopoulos S (2011) Face recognition via local sparse coding. In: Proceedings of the ICCV, pp 1647–1652

  4. Gao S, Tsang I, Chia L (2010) Kernel sparse representation for image classification and face recognition. In: Proceedings of the ECCV, pp 1–14

  5. Yang M, Zhang L (2010) Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary. In: Proceedings of the ECCV, pp 448–461

  6. Yang M, Zhang L, Yang J, Zhang D (2011) Robust sparse coding for face recognition. In: Proceedings of the ICCV, pp 625–632

  7. Yang M, Zhang L, Yang J, Zhang D (2013) Regularized robust coding for face recognition. IEEE Trans Image Process 22(5):1753–1766

    Article  MathSciNet  MATH  Google Scholar 

  8. He R, Zheng WS, Hu BG (2011) Maximum correntropy criterion for robust face recognition. IEEE Trans Pattern Anal Mach Intell 33(8):1561–1576

    Article  Google Scholar 

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

  10. Yang M, Zhang L, Zhang D, Wang S (2012) Relaxed collaborative representation for pattern classification. In: Proceedings of the ICCV, pp 2224–2231

  11. 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  MathSciNet  Google Scholar 

  12. Mi J-X, Liu J-X (2013) Face recognition using sparse representation-based classification on K-nearest subspace. PLoS ONE 8(3):e59430. doi:10.1371/journal.pone.0059430

    Article  Google Scholar 

  13. Yang M, Zhang L, Feng X, Zhang D (2011) Fisher discrimination dictionary learning for sparse representation. In: Proceedings of the ICCV, pp 543–550

  14. Feng Z, Yang M, Zhang L, Liu Y, Zhang D (2013) Joint discriminative dimensionality reduction and dictionary learning for face recognition. Pattern Recogn 46(8):2134–2143

    Article  Google Scholar 

  15. Gu S, Zhang L, Zuo W et al (2014) Projective dictionary pair learning for pattern classification. In: Proceeding of advances in neural information processing systems, pp 793–801

  16. Cai S, Zuo W, Zhang L (2014) Support vector guided dictionary learning. In: Proceedings of the European conference on computer vision, pp 624–639

  17. Li Z, Lai Z, Xu Y et al (2015) A locality-constrained and label embedding dictionary learning algorithm for image classification. IEEE Trans Neural Netw Learn Syst. doi:10.1109/TNNLS.2015.2508025

    Google Scholar 

  18. Cai S, Zhang L, Zuo W, Feng X (2016) A probabilistic collaborative representation based approach for pattern classification. In CVPR, pp 2950–2959

  19. Lades M, Vorbrüggen JC, Buhmann J et al (1993) Distortion invariant object recognition in the dynamic link architecture. IEEE Trans Comput 42(3):300–311

    Article  Google Scholar 

  20. Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image Process 11(4):467–476

    Article  Google Scholar 

  21. Shen L, Bai L (2006) A review on Gabor wavelets for face recognition. Pattern Anal Appl 9(10):273–292

    Article  MathSciNet  Google Scholar 

  22. Timo A, Abdenour H, Matti P (2004) Face recognition with local binary patterns. In: Proceedings of the ECCV, pp 469–481

  23. Ojala T, Pietikäinen M, Mäenpää T (2002) Multi-resolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  MATH  Google Scholar 

  24. Zhang W, Shan S, Gao W et al (2005) Local Gabor binary pattern histogram sequence (LGBPHS): A novel non-statistical model for face representation and recognition. In: Proceedings of the ICCV, pp 786–791

  25. Zhang W, Shan S, Chen X, Gao W (2009) Are Gabor phases really useless for face recognition?. Pattern Anal Appl 12(3):301–307

    Article  MathSciNet  Google Scholar 

  26. Zhang B, Shan S, Chen X, Gao W (2007) Histogram of Gabor phase patterns (HGPP): a novel object representation approach for face recognition. IEEE Trans Image Process 16(1):57–68

    Article  MathSciNet  Google Scholar 

  27. Xie SF, Shan SG, Chen XL, Chen J (2010) Fusing local patterns of Gabor magnitude and phase for face recognition. IEEE Trans Image Process 19(5):1349–1361

    Article  MathSciNet  MATH  Google Scholar 

  28. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041

    Article  MATH  Google Scholar 

  29. Turk M, Pentland A (1991) Eigenfaces for recognition. J. Cognitive. Neuroscience 3(1):71–86

    Google Scholar 

  30. Belhumeur PN, Hespanha JP, Kriengman DJ (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  31. Borgi MA, El’arbi M, Labate D, Amar CB (2015) Regularized directional feature learning for face recognition. Multimedia Tools Appl 74(24):11281–11295

    Article  Google Scholar 

  32. Borgi MA, Labate D, El’Arbi M, Amar CB (2014) Regularized Shearlet network for face recognition using single sample per person. In: Proceedings of the ICASSP, pp 514–518

  33. Borgi MA, Labate D, El'arbi M, Amar CB (2014) ShearFace: efficient extraction of anisotropic features for face recognition. In: Proceedings of the ICPR, pp 1806–1811

  34. Borgi MA, Labate D, El'arbi M, Amar CB (2014) Sparse multi-regularized shearlet-network using convex relaxation for face recognition. In: Proceedings of the ICPR, pp 4636–4641

  35. Borgi MA, Labate D, El'arbi M, Amar CB (2013) Shearlet network-based sparse coding augmented by facial texture features for face recognition. In: Proceedings of the ICIAP, pp 611–620

  36. Yang M, Zhang L, Shiu SC, Zhang D (2013) Robust kernel representation with statistical local features for face recognition. IEEE Trans Neural Netw Learn Syst 24(6):900–912

    Article  Google Scholar 

  37. Nguyen TP, Vu NS, Manzanera A (2016) Statistical binary patterns for rotational invariant texture classification. Neurocomputing 173:1565–1577

    Article  Google Scholar 

  38. Guo ZH, Zhang L, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657–1663

    Article  MathSciNet  MATH  Google Scholar 

  39. Ojala T, Maenpaa T, Pietikainen M et al (2002) Outex—new framework for empirical evaluation of texture analysis algorithms. In: Proceedings of the ICPR, pp 701–706

  40. Martinez A, Benavente R (1998) The AR face database. CVC technical report 24

  41. Georghiades A, Belhumeur P, Kriegman D (2001) From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE PAMI 23(6):643–660

    Article  Google Scholar 

  42. Lee K, Ho J, Kriegman D (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE PAMI 27(5):684–698

    Article  Google Scholar 

  43. Li SZ, Lu J (1999) Face recognition using nearest feature line method. IEEE Trans Neural Netw 10(2):439–443

    Article  Google Scholar 

  44. 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 

  45. Wang JJ, Yang JC et al (2010) Locality-constrained linear coding for image classification. In: Proceedings of the CVPR, pp 3360–3371

  46. Gross R, Matthews I et al (2010) Multi-PIE. Image Vis Comput 28(5):807–813

    Article  Google Scholar 

  47. Georgia Tech Face Database (2007). http://www.anefian.com/face_reco.htm

  48. Phillips PJ, Flynn PJ et al (2005) Overview of the face recognition grand challenge. In: Proceedings of the CVPR, pp 947–954

  49. Zhang L, Yang M et al (2011) Collaborative representation based classification for face recognition. Technical report. arXiv: 1204.2358

  50. Borgi MA, El’arbi M, Labate D, Amar CB (2014) Face, gender and race classification using multi-regularized features learning. In: Proceedings of the ICIP, pp 5277–5281

  51. Thomaz E, Giraldi GA (2010) A new ranking method for principal components analysis and its application to face image analysis. Image Vis Comput 28(6):902–913

    Article  Google Scholar 

  52. 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 

  53. Nilsback M, Zisserman A (2006) A visual vocabulary for flower classification. In: Proceedings of the CVPR, pp 1447–1454

  54. Yuan XT, Yan SC (2010) Visual classification with multitask joint sparse representation. In: Proceedings of the CVPR, pp 3493–3500

  55. Nilsback M, Zisserman A (2008) Automated flower classification over a large number of classes. In: Proceedings of the ICCVGIP, pp 722–729

  56. Gehler P, Nowozin S (2009) On feature combination for multiclass object classification. In: Proceedings of the ICCV, pp 221–228

  57. Rodriguez MD, Ahmed J, Shah M (2008) Action MACH a spatio-temporal maximum average correlation height filter for action recognition. In: Proceedings of the CVPR

  58. Sadanand S, Corso JJ (2012) Action bank: a high-level representation of activity in video. In: Proceedings of the CVPR, pp 1234–1241

  59. Yao A, Gall J, Van Gool LJ (2010) A hough transform-based voting framework for action recognition. In: Proceedings of the CVPR, pp 2061–2068

  60. Yeffet L, Wolf L (2009) Local trinary patterns for human action recognition. In: Proceedings of the ICCV, pp 492–497

  61. Wang H, Ullah MM et al (2009) Evaluation of local spatio-temporal features for action recognition. In: Proceedings of the BMVC, pp 1–11

Download references

Author information

Authors and Affiliations

  1. Research Groups on Intelligent Machines, University of Sfax, BP 1173, 3038, Sfax, Tunisia

    Mohamed Anouar Borgi & Chokri Ben Amar

  2. Aix Marseille Université, CNRS, ENSAM, LSIS, UMR 7296, 13397, Marseille, France

    Thanh Phuong Nguyen

  3. Université de Toulon, CNRS, LSIS, UMR 7296, 83957, La Garde, France

    Thanh Phuong Nguyen

  4. Department of Mathematics, University of Houston, Houston, TX, 77204, USA

    Demetrio Labate

Authors
  1. Mohamed Anouar Borgi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thanh Phuong Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Demetrio Labate
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chokri Ben Amar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed Anouar Borgi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Borgi, M.A., Nguyen, T.P., Labate, D. et al. Statistical binary patterns and post-competitive representation for pattern recognition. Int. J. Mach. Learn. & Cyber. 9, 1023–1038 (2018). https://doi.org/10.1007/s13042-016-0625-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-016-0625-9

Keywords

Search

Navigation