Advertisement

Local generic representation for patch uLBP-based face recognition with single training sample per subject

  • Taher Khadhraoui
  • Mohamed Anouar Borgi
  • Faouzi Benzarti
  • Chokri Ben Amar
  • Hamid Amiri
Article

Abstract

In this paper, we propose a novel paradigm of Patch uniform Local Binary Patterns (PuLBP) based Local Generic Representation (LGR) for face recognition. Indeed, we introduce a new block in which an uLBP is used to approximate both reference and variation subsets. Thus, we concentrate on the challenging problem of a single sample per person in a gallery set. Particularly, the main problem is whether only one training subject per class is available. One of the novelties of our technique is to generate virtual samples of each subject. The new sample generic image in a gallery set is adopted to produce the intra-personal variations of different individuals. We illustrate the experimental results of our new algorithm on different benchmark databases, including the AR face database, the Extended Yale B face database, the FRGC database and the FEI database.

Keywords

Face recognition Uniform local binary patterns Local generic representation Single training sample per subject 

Notes

Acknowledgements

The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program. D. L acknowledges partial support by NSF DMS 1005799 and DMS 1008900.

References

  1. 1.
    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):2037–2041CrossRefMATHGoogle Scholar
  2. 2.
    Borgi MA, Labate D, El’Arbi M, Amar CB (2014) Regularized shearlet network for face recognition using single sample per person. In: 2014 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 514–518Google Scholar
  3. 3.
    Borgi MA, El’Arbi M, Labate D, Amar CB (2015) Regularized directional feature learning for face recognition. Multimed Tool Appl 74(24):11,281–11,295CrossRefGoogle Scholar
  4. 4.
    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):269–279CrossRefGoogle Scholar
  5. 5.
    Borgi MA, Nguyen TP, Labate D, Amar CB (2016) Statistical binary patterns and post-competitive representation for pattern recognition. Int J Mach Learn Cybern:1–16Google Scholar
  6. 6.
    Cevikalp H (2010) New clustering algorithms for the support vector machine based hierarchical classification. Pattern Recogn Lett 31(11):1285–1291CrossRefGoogle Scholar
  7. 7.
    Chen S, Liu J, Zhou ZH (2004) Making flda applicable to face recognition with one sample per person. Pattern Recogn 37(7):1553–1555CrossRefGoogle Scholar
  8. 8.
    Chen L, Man H, Nefian AV (2005) Face recognition based on multi-class mapping of fisher scores. Pattern Recogn 38(6):799–811CrossRefGoogle Scholar
  9. 9.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297MATHGoogle Scholar
  10. 10.
    Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27CrossRefMATHGoogle Scholar
  11. 11.
    Deng W, Hu J, Guo J (2012) Extended src: undersampled face recognition via intraclass variant dictionary. IEEE Trans Pattern Anal Mach Intell 34(9):1864–1870CrossRefGoogle Scholar
  12. 12.
    Fan Z, Ni M, Zhu Q, Sun C, Kang L (2015) L 0-norm sparse representation based on modified genetic algorithm for face recognition. J Vis Commun Image Represent 28:15–20CrossRefGoogle Scholar
  13. 13.
    Final R Captura e alinhamento de imagens: Um banco de faces brasileiroGoogle Scholar
  14. 14.
    Gao S, Tsang IWH, Chia LT (2010) Kernel sparse representation for image classification and face recognition. In: European conference on computer vision. Springer, pp 1–14Google Scholar
  15. 15.
    He R, Tan T, Wang L, Zheng WS (2012) l 2, 1 regularized correntropy for robust feature selection. In: 2012 IEEE conference on Computer vision and pattern recognition (CVPR). IEEE, pp 2504–2511Google Scholar
  16. 16.
    Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425CrossRefGoogle Scholar
  17. 17.
    Huang K, Aviyente S (2006) Sparse representation for signal classification. In: Advances in neural information processing systems, pp 609–616Google Scholar
  18. 18.
    Khadhraoui T, Benzarti F, Amiri H (2014) Multimodal hybrid face recognition based on score level fusion using relevance vector machine. In: 2014 IEEE/ACIS 13th International Conference on Computer and Information Science (ICIS). IEEE, pp 211–215Google Scholar
  19. 19.
    Khadhraoui T, Benzarti F, Amiri H (2014) New approach on pca-based 3d face recognition and authentication. In: 2014 15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). IEEE, pp 1–5Google Scholar
  20. 20.
    Khorsandi RS (2015) Sparse representation and dictionary learning for biometrics and object trackingGoogle Scholar
  21. 21.
    Kumar R, Banerjee A, Vemuri BC, Pfister H (2011) Maximizing all margins: pushing face recognition with kernel plurality. In: 2011 International conference on computer vision. IEEE, pp 2375–2382Google Scholar
  22. 22.
    Kumar P, Krishna VV, Kumar VV (2016) A dynamic transform noise resistant uniform local binary pattern (dtnr-ulbp) for age classification. International Journal of Applied Engineering Research, ISSN, pp 0973–4562Google Scholar
  23. 23.
    Lee W, Cheon M, Hyun CH, Park M (2013) Best basis selection method using learning weights for face recognition. Sensors 13(10):12,830–12,851CrossRefGoogle Scholar
  24. 24.
    Liu W, Pokharel PP, Príncipe JC (2007) Correntropy: properties and applications in non-gaussian signal processing. IEEE Trans Signal Process 55 (11):5286–5298MathSciNetCrossRefGoogle Scholar
  25. 25.
    Lu C, Tang J, Lin M, Lin L, Yan S, Lin Z (2013) Correntropy induced l2 graph for robust subspace clustering. In: Proceedings of the IEEE international conference on computer vision, pp 1801–1808Google Scholar
  26. 26.
    Lu J, Tan YP, Wang G (2013) Discriminative multimanifold analysis for face recognition from a single training sample per person. IEEE Trans Pattern Anal Mach Intell 35(1):39–51CrossRefGoogle Scholar
  27. 27.
    Lucey P, Cohn JF, Kanade T, Saragih J, Ambadar Z, Matthews I (2010) The extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE Computer society conference on computer vision and pattern recognition-workshops. IEEE, pp 94–101Google Scholar
  28. 28.
    Mäenpää T, Pietikäinen M (2003) Multi-scale binary patterns for texture analysis. Image Anal 2749:267–275Google Scholar
  29. 29.
    Maenpaa T, Pietikainen M, Viertola J (2002) Separating color and pattern information for color texture discrimination. In: Proceedings of the 16th international conference on Pattern recognition, 2002, vol 1. IEEE, pp 668–671Google Scholar
  30. 30.
    Marcolin F, Vezzetti E (2017) Novel descriptors for geometrical 3d face analysis. Multimed Tool Appl 76(12):13,805–13,834CrossRefGoogle Scholar
  31. 31.
    Martinez AM (1998) The ar face database. CVC Technical Report 24Google Scholar
  32. 32.
    Nikolova M, Ng MK (2005) Analysis of half-quadratic minimization methods for signal and image recovery. SIAM J Sci Comput 27(3):937–966MathSciNetCrossRefMATHGoogle Scholar
  33. 33.
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefMATHGoogle Scholar
  34. 34.
    Phillips PJ, Flynn PJ, Scruggs T, Bowyer KW, Chang J, Hoffman K, Marques J, Min J, Worek W (2005) Overview of the face recognition grand challenge. In: 2005 IEEE Computer society conference on Computer Vision and Pattern Recognition (CVPR’05), vol 1. IEEE, pp 947–954Google Scholar
  35. 35.
    Pietikäinen M, Hadid A, Zhao G, Ahonen T (2011) Local binary patterns for still images. In: Computer vision using local binary patterns. Springer, pp 13–47Google Scholar
  36. 36.
    Semwal VB, Singha J, Sharma PK, Chauhan A, Behera B (2016) An optimized feature selection technique based on incremental feature analysis for bio-metric gait data classification. Multimed Tool Appl:1–19Google Scholar
  37. 37.
    Semwal VB, Mondal K, Nandi GC (2017) Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach. Neural Comput Applic 28(3):565–574CrossRefGoogle Scholar
  38. 38.
    Shahdi SO, Abu-Bakar SAR (2011) Multi-color ulbp with wavelet transform in invariant pose face recognition. In: 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). IEEE, pp 52–57Google Scholar
  39. 39.
    Su Y, Shan S, Chen X, Gao W (2010) Adaptive generic learning for face recognition from a single sample per person. In: CVPR, pp 2699–2706Google Scholar
  40. 40.
    Tibshirani R (2011) Regression shrinkage and selection via the lasso: a retrospective. J R Stat Soc Ser B (Stat Methodol.) 73(3):273–282MathSciNetCrossRefGoogle Scholar
  41. 41.
    Vezzetti E, Marcolin F, Tornincasa S, Maroso P (2016) Application of geometry to rgb images for facial landmark localisation-a preliminary approach. Int J Biometrics 8(3-4):216–236CrossRefGoogle Scholar
  42. 42.
    Wang C, Huang K (2015) How to use bag-of-words model better for image classification. Image Vis Comput 38:65–74CrossRefGoogle Scholar
  43. 43.
    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):210–227CrossRefGoogle Scholar
  44. 44.
    Xu J, Yang J (2013) A nonnegative sparse representation based fuzzy similar neighbor classifier. Neurocomputing 99:76–86CrossRefGoogle Scholar
  45. 45.
    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. Inf Sci 238:138–148MathSciNetCrossRefGoogle Scholar
  46. 46.
    Yang M, Zhang L, Yang J, Zhang D (2010) Metaface learning for sparse representation based face recognition. In: 2010 IEEE International conference on image processing. IEEE, pp 1601–1604Google Scholar
  47. 47.
    Yang M, Zhang L, Zhang D, Wang S (2012) Relaxed collaborative representation for pattern classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 2224–2231Google Scholar
  48. 48.
    Yang M, Van Gool L, Zhang L (2013) Sparse variation dictionary learning for face recognition with a single training sample per person. In: Proceedings of the IEEE international conference on computer vision, pp 689–696Google Scholar
  49. 49.
    Yang M, Zhang L, Yang J, Zhang D (2013) Regularized robust coding for face recognition. IEEE Trans Image Process 22(5):1753–1766MathSciNetCrossRefMATHGoogle Scholar
  50. 50.
    Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: whichx helps face recognition?. In: 2011 International conference on computer vision. IEEE, pp 471–478Google Scholar
  51. 51.
    Zhao G, Pietikainen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans Pattern Anal Mach Intell 29(6):915–928CrossRefGoogle Scholar
  52. 52.
    Zhu P, Zhang L, Hu Q, Shiu SC (2012) Multi-scale patch based collaborative representation for face recognition with margin distribution optimization. In: European conference on computer vision. Springer, pp 822–835Google Scholar
  53. 53.
    Zhu P, Yang M, Zhang L, Lee IY (2014) Local generic representation for face recognition with single sample per person. In: Asian conference on computer vision. Springer, pp 34–50Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Taher Khadhraoui
    • 1
  • Mohamed Anouar Borgi
    • 2
  • Faouzi Benzarti
    • 1
  • Chokri Ben Amar
    • 2
  • Hamid Amiri
    • 1
  1. 1.SITI Laboratory, National Engineering School of Tunis (ENIT)University of Tunis El ManarTunisTunisia
  2. 2.Research Groups on Intelligent MachinesUniversity of SfaxSfaxTunisia

Personalised recommendations