Advertisement

Local binary pattern-based discriminant graph construction for dimensionality reduction with application to face recognition

  • Bo YangEmail author
  • Qian-zhong Li
Article
  • 23 Downloads

Abstract

Graph construction has attracted increasing interest in recent years due to its key role in many dimensionality reduction (DR) algorithms. On the other hand, our previous study shows that the Local-Binary-Pattern Image (LBPI) representation is a more powerful discriminant and is invariant to monotonic gray level changes. Here, we attempt to construct a discriminant graph for DR in the LBPI representation space. We call the graph the Local-Binary-Image Discriminant (LBID) graph and further incorporate the LBID graph into the Locality Preserving Projection (LPP) to develop an enhanced algorithm - Local Binary Image Discriminant Preserving Projection (LBIDPP). Meanwhile, we also construct a Local-Binary-Histogram (LBH) graph in LBP histogram space and obtain the Local Binary Histogram Preserving Projection (LBHPP) algorithm and compare these to the LBID graph and LBIDPP. It is worth noting that LBIDPP is not a simple combination of the two feature extractions LBP and LPP, i.e., LBP + LPP. LBIDPP inherits the attractive properties of the LBP and LPP. The experiments on face recognition validate the effectiveness and feasibility of the LBID graph and LBIDPP.

Keywords

Local binary pattern Dimensionality reduction Face recognition Graph embedding 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61363051), China Postdoctoral Science Foundation (Nos.2013 M540217), Program of Higher-Level Talents of Inner Mongolia University (Nos. 115118, 135113).

References

  1. 1.
    Ahonen T, Hadid A, Pietikäinen M (2004) Face recognition with local binary patterns, in 8th proceedings of European conference on computer vision (ECCV04), LNCS 3021, Springer, 69–481Google Scholar
  2. 2.
    Ahonen T, Pietikäinen M, Hadid A, Mäenpää T (2004) Face recognition based on the appearance of local regions, the 17th International Conference on Pattern Recognition (ICPR), Cambridge, Uk, 153–156Google Scholar
  3. 3.
    Ahonen T, Hadid A, Pietikäinen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intel (TPAMI) 28:2037–2041CrossRefGoogle Scholar
  4. 4.
    Aoyama K, Saito K, Sawada H, Ueda N (2011) Fast approximate similarity search based on degree-reduced neighborhood graphs. in KDD:1055–1063Google Scholar
  5. 5.
    Argyriou A, Herbster M, Pontil M (2005) Combining graph laplacians for semi-supervised learning. Adv Neural Inform Proc Syst (NIPS) 18:67–74Google Scholar
  6. 6.
    Belkin M, Niyogi P (2001) Laplacian Eigenmaps and spectral techniques for embedding and clustering, advances in neural information processing systems 14 (NIPS), Vancouver, British Columbia, CanadaGoogle Scholar
  7. 7.
    Belkin M, Niyogi P (2003) Laplacian Eigenmaps for dimensionality redaction and data representation. Neural Comput 15:1373–1396CrossRefGoogle Scholar
  8. 8.
    Cai D, He X, Han J, Semi-Supervised Discriminant Analysis, (2007) IEEE international conference on computer vision (ICCV), Rio de Janeiro, BrazilGoogle Scholar
  9. 9.
    Duda RO, Hart PE, Stork DG (2001) Pattern classification, second ed., John, New York.Google Scholar
  10. 10.
    Fathi A, Naghsh-Nilchi A (2012) Noise tolerant local binary pattern operator for efficient texture analysis. Pattern Recogn Lett 33:1093–1100CrossRefGoogle Scholar
  11. 11.
    Garcia MA, Balu D (2007) Supervised texture classification by integration of multiple texture methods and evaluation windows. Image Vis Comput 25:1091–1106CrossRefGoogle Scholar
  12. 12.
    Golub GH, Van Loan CF, (1996) Matrix Computations, The Johns Hopkins University Press, Baltimore, USA, third edition.Google Scholar
  13. 13.
    Harwood D, Ojala T, Pietikäinen M, Kelman S, Davis S (1993) Texture classification by center-symmetric auto-correlation, using Kullback discrimination of distributions, Technical report, Computer Vision Laboratory, Center for Automation Research, University of Maryland, College Park, Maryland. CAR-TR-678Google Scholar
  14. 14.
    He X, Niyogi P (2003) Locality Preserving Projections, Proc. 16th Conf. Neural Information Processing Systems (NIPS).Google Scholar
  15. 15.
    He X, Cai D, Yan S, Zhang H (2005) Neighborhood preserving embedding, Proc. in International Conference on Computer Vision (ICCV)Google Scholar
  16. 16.
    Jebara T, J. Wang and S. Chang, (2009) Graph construction and b-matching for semi-supervised learning, In proceedings of the 26th international conference on machine learning (ICML), Montreal, CanadaGoogle Scholar
  17. 17.
    Levin K, Lyzinski V (2017) Laplacian Eigenmaps from sparse, Noisy similarity measurements. IEEE Trans Signal Process 65(8):1988–2003MathSciNetCrossRefGoogle Scholar
  18. 18.
    Liu J, Ma A, Li J (2017) Low-rank representation with graph constraints for robust visual tracking. IEICE Trans Inf Syst E100D(6):1325–1338CrossRefGoogle Scholar
  19. 19.
    Maier M, Luxburg U (2008) Influence of graph construction on graph-based clustering measures, the neural information processing systems (NIPS), 21: 1025–1032Google Scholar
  20. 20.
    Martinez A, Benavente R, (1998) The AR Face Database, CVC Technical Report #24Google Scholar
  21. 21.
    Moses Y, Adini Y, Ullman S (1994) Face recognition: the problem of compensating for changes in illumination direction. Eur Conf Comput Vis:286–296Google Scholar
  22. 22.
    Nanni L, Brahnam S, Lumini A (2012) A simple method for improving local binary patterns by considering non-uniform patterns. Pattern Recogn 45:3844–3852CrossRefGoogle Scholar
  23. 23.
    Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Patt Anal Mach Intel (TPAMI) 24:971–987CrossRefGoogle Scholar
  24. 24.
    O. M. Parkhi, A. Vedaldi, and A. Zisserman, (2015) Deep face recognition, In British Machine Vision ConferenceGoogle Scholar
  25. 25.
    Qiao L, Chen S, Tan X (2010) Sparsity preserving projections with applications to face recognition. Pattern Recogn 43:331–341CrossRefGoogle Scholar
  26. 26.
    Roweis S, Saul L (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323–2326CrossRefGoogle Scholar
  27. 27.
    Sun Y, Wang S, Liu Q (2017) Hypergraph embedding for spatial-spectral joint feature extraction in hyperspectral images. Remote Sens 9(5):506CrossRefGoogle Scholar
  28. 28.
    Torre B, Lorenzoni M, Bicego M, Cristani M, Murino V, Diaspro A (2011) Principal component analysis in dynamic force spectroscopy. GIT Imaging Microsc 13:26–28Google Scholar
  29. 29.
    J. Wang, J. Wang, G. Zeng, Z. Tu, R. Gan, and S. Li, (2012) Scalable k-nn graph construction for visual descriptors, in CVPR, 1106–1113.Google Scholar
  30. 30.
    Wang R, Nie F, Hong R (2017) Fast and orthogonal locality preserving projections for dimensionality reduction. IEEE Trans Image Process 26(10):5019–5030MathSciNetCrossRefGoogle Scholar
  31. 31.
    Woo S, Lee C (2018) Incremental feature extraction based on decision boundaries. Pattern Recogn 77:65–74CrossRefGoogle Scholar
  32. 32.
    Wu X, He R, Sun Z (2015), A lightened cnn for deep face representation, 2015 IEEE Conference on IEEE Computer Vision and Pattern Recognition (CVPR). Vol. 4Google Scholar
  33. 33.
    Xiao Y, Xia L (2016) Human action recognition using modified slow feature analysis and multiple kernel learning. Multimed Tools Appl 75(21):13041–13056CrossRefGoogle Scholar
  34. 34.
    Yan S, Xu D, Zhang B, Zhang H, Yang Q, Lin S (2007) Graph embedding and extension: a general framework for dimensionality reduction. IEEE Trans Patt Anal Mach Intel (TPAMI) 29:40–51CrossRefGoogle Scholar
  35. 35.
    Yan Y, Wang H, Li C, Yang C, Zhong B (2013) An effective unconstrained correlation filter and its Kernelization for face recognition. Neurocomputing 119:201–211CrossRefGoogle Scholar
  36. 36.
    Yang B, Chen S (2010) Sample-dependent graph construction with application to dimensionality reduction. Neurocomputing 74:301–314CrossRefGoogle Scholar
  37. 37.
    Yang B, Chen S (2010) Disguised discrimination of locality-based unsupervised dimensionality reduction. Int J Pattern Recognit Artif Intell 24:1011–1025CrossRefGoogle Scholar
  38. 38.
    Yang B, Chen S (2013) A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image. Neurocomputing 120:365–379CrossRefGoogle Scholar
  39. 39.
    Yang J, Zhang D, Frangi AF, Yang J-y (2004) Two-dimensional PCA: a new approach to face representation and recognition. IEEE Trans Pattern Anal Mach Intel 26:131–137CrossRefGoogle Scholar
  40. 40.
    Yang J, Zhang D, Yang J-Y, Niu B (2007) Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Trans Pattern Anal Mach Intel (TPAMI) 29:650–664CrossRefGoogle Scholar
  41. 41.
    Yun F, Ma Y (2013) Graph embedding for pattern classification, SpringerGoogle Scholar
  42. 42.
    Zhang L, Chen S, Qiao L (2012) Graph optimization for dimensionality reduction with sparsity constraints. Pattern Recogn 45:1205–1210CrossRefGoogle Scholar
  43. 43.
    Zhu X, (2008) Semi-supervised learning literature survey, computer sciences technical report 1530, University of Wisconsin-MadisonGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Computer ScienceInner Mongolia UniversityHohhotPeople’s Republic of China
  2. 2.School of Physical Science and TechnologyHohhotPeople’s Republic of China
  3. 3.Inner Mongolia Key Laboratory of Data Mining and Knowledge EngineeringHohhotPeople’s Republic of China

Personalised recommendations