Skip to main content
SpringerLink
Log in

Fusing hierarchical multi-scale local binary patterns and virtual mirror samples to perform face recognition

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The horizontal axis-symmetrical nature of faces is useful and interesting, which has successfully applied in face detection. In general, images of faces are not strictly captured under a frontal and natural pose. It implies that extra training samples could be generated by means of the symmetry of the face. In this paper, we develop a framework that fuses virtual mirror synthesized training samples as bases and hierarchical multi-scale local binary patterns (LBP) features for classification. More specifically, in the first stage of proposed method, sampling uncertainty of the linear approximation model is alleviated effectively by constructing extra synthesized mirror training samples which generated from original images. Subsequently, in the second stage, features are extracted from the above dictionary using a hierarchical multi-scale LBP scheme. In the third stage, we combine the synthesized samples and original ones together to describe a test sample and simultaneously determine the relatively informative training samples by means of exploiting the reconstruction deviation of all the dictionary atoms. Besides, the introduced sparse coding process with weak l 1 constraint has superior competitiveness that the accuracy has been improved, while the complexity has been fallen. Ultimately, the following step determines again a new decomposition coefficient of remaining samples, which makes final decision of the classification. Experimental results that are engaged in various benchmark face databases have demonstrated the effectiveness of our algorithm.

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
Fig. 12
Fig. 13
Fig. 14

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 (CSUR) 35(4):399–458

    Article  Google Scholar 

  2. Zhou J, Ji Z, Shen L, Zhu Z, Chen S (2011) PSO based memetic algorithm for face recognition gabor filters selection. In: IEEE workshop on memetic computing (MC), 2011, Paris, pp 1–6

  3. Yang J, Zhang D, Frangi AF et al (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. Pattern Anal Mach Intell IEEE Trans 26(1):131–137

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Lee KC, Ho J, Kriegman D (2005) Acquiring linear subspaces for face recognition under variable lighting. Pattern Anal Mach Intell IEEE Trans 27(5):684–698

    Article  Google Scholar 

  6. Pentland A, Moghaddam B, Starner T (1994) View-based and modular eigenspaces for face recognition. In: Proceedings of CVPR’94, IEEE computer society conference on computer vision and pattern recognition, 1994, Seattle, WA, pp 84–91

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

    Article  Google Scholar 

  8. He X, Yan S, Hu Y et al (2005) Face recognition using Laplacianfaces. Pattern Anal Mach Intell IEEE Trans 27(3):328–340

    Article  Google Scholar 

  9. Zhang J, Li SZ, Wang J (2004) Nearest manifold approach for face recognition. In: Proceedings of sixth IEEE international conference on automatic face and gesture recognition, 2004, pp 223–228

  10. Blanz V, Vetter T (2003) Face recognition based on fitting a 3D morphable model. Pattern Anal Mach Intell IEEE Trans 25(9):1063–1074

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Wright J, Ma Y, Mairal J et al (2010) Sparse representation for computer vision and pattern recognition. Proc IEEE 98(6):1031–1044

    Article  Google Scholar 

  13. Gao S, Tsang IWH, Chia LT (2010) Kernel sparse representation for image classification and face recognition. Comput Vis ECCV 2010 2010:1–14

    Article  Google Scholar 

  14. Huang K, Aviyente S (2006) Sparse representation for signal classification. Adv Neural Inf Process Syst 609–616

  15. Moon H, Miller ML (2009) Estimating facial pose from a sparse representation. US Patent 7,526,123[P], 28 April 2009

  16. Yang M, Zhang L (2010) Gabor feature based sparse representation for face recognition with gabor occlusion dictionary. Comput Vis ECCV 2010 2010:448–461

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  18. Zhang L, Yang M, Feng X, et al (2012) Collaborative representation based classification for face recognition. arXiv preprint arXiv:1204.2358

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

    Article  Google Scholar 

  20. Zhang G, Huang X, Li SZ et al (2005) Boosting local binary pattern (LBP)-based face recognition. In: Advances in biometric person authentication, vol 3338. Springer, Berlin, Heidelberg, pp 179–186

    Chapter  Google Scholar 

  21. Huang D, Shan C, Ardabilian M et al (2011) Local binary patterns and its application to facial image analysis: a survey. Syst Man Cybern Part C Appl Rev IEEE Trans 41(6):765–781

    Article  Google Scholar 

  22. Guo Z, Zhang D, Mou X (2010) Hierarchical multiscale LBP for face and palmprint recognition. In: 17th IEEE international conference on image processing (ICIP), 2010, Hong Kong, pp 4521–4524

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

    Article  Google Scholar 

  24. Vetter T, Poggio T (1997) Linear object classes and image synthesis from a single example image. Pattern Anal Mach Intell IEEE Trans 19(7):733–742

    Article  Google Scholar 

  25. Sharma A, Dubey A, Tripathi P et al (2010) Pose invariant virtual classifiers from single training image using novel hybrid-eigenfaces. Neurocomputing 73(10):1868–1880

    Article  Google Scholar 

  26. Xu Y, Zhu X, Li Z et al (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 

  27. Xu Y, Zhu Q, Fan Z et al (2013) Using the idea of the sparse representation to perform coarse-to-fine face recognition. Inf Sci 238:138–148

    Article  MathSciNet  Google Scholar 

  28. Xu Y, Zuo W, Fan Z (2012) Supervised sparse representation method with a heuristic strategy and face recognition experiments. Neurocomputing 79:125–131

    Article  Google Scholar 

  29. Xu Y, Fang X, Li X et al (2014) Data uncertainty in face recognition. IEEE Trans Cybern 44(11):1950–1961

    Article  Google Scholar 

  30. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B (Methodological) 58:267–288

    MATH  MathSciNet  Google Scholar 

  31. Mallat SG, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. Signal Process IEEE Trans 41(12):3397–3415

    Article  MATH  Google Scholar 

  32. Ebrahimi-Moghadam A, Shirani S (2007) Matching pursuit-based region-of-interest image coding. Image Process IEEE Trans 16(2):406–415

    Article  MathSciNet  Google Scholar 

  33. Rubinstein R, Zibulevsky M, Elad M (2008) Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit. CS Tech 40:1–15

    Google Scholar 

  34. Pati YC, Rezaiifar R, Krishnaprasad PS (1993) Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: 1993 conference record of the twenty-seventh Asilomar conference on signals, systems and computers, Pacific Grove, CA, vol 1, pp 40–44

  35. Needell D, Tropp JA (2009) CoSaMP: iterative signal recovery from incomplete and inaccurate samples. Appl Comput Harmon Anal 26(3):301–321

    Article  MATH  MathSciNet  Google Scholar 

  36. Candes EJ, Tao T (2005) Decoding by linear programming. Inf Theory IEEE Trans 51(12):4203–4215

    Article  MATH  MathSciNet  Google Scholar 

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

  38. Yang M, Zhang D, Wang S (2012) Relaxed collaborative representation for pattern classification. In: IEEE conference on computer vision and pattern recognition (CVPR), 2012, Providence, RI, pp 2224–2231

  39. ORL database. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

  40. FERET database. http://www.itl.nist.gov/iad/humanid/feret/feret−master.html

  41. Yale database. http://vision.ucsd.edu/leekc/ExtYaleDatabase/ExtYaleB.html

  42. AR database. http://cobweb.ecn.purdue.edu/∼aleix/aleix−face−DB.html

  43. GT database. http://www.anefian.com/research/face_reco.htm

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

    Article  Google Scholar 

  45. Mailhé B, Gribonval R, Vandergheynst P et al (2011) Fast orthogonal sparse approximation algorithms over local dictionaries. Sig Process 91(12):2822–2835

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Science Foundation of China (Grant Nos. 61100116 and 61125305), the Natural Science Foundation of Jiangsu Province (Grant Nos. BK2012700 and BK2011492), the Foundation of Artificial Intelligence Key Laboratory of Sichuan Province (Grant No. 2012RZY02), the Open Project Program of the State Key Laboratory of CAD&CG of Zhejiang University (Grant No. A1418) and the Foundation of Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Zi Liu & Zhenmin Tang

  2. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Xiaoning Song

Authors
  1. Zi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoning Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhenmin Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zi Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Z., Song, X. & Tang, Z. Fusing hierarchical multi-scale local binary patterns and virtual mirror samples to perform face recognition. Neural Comput & Applic 26, 2013–2026 (2015). https://doi.org/10.1007/s00521-015-1863-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-015-1863-6

Keywords

Search

Navigation