Advertisement

Anchor-based manifold binary pattern for finger vein recognition

  • Haiying Liu
  • Gongping YangEmail author
  • Lu Yang
  • Kun Su
  • Yilong Yin
Research Paper
  • 8 Downloads

Abstract

This paper proposes a novel learning method of binary local features for recognition of the finger vein. The learning methods existing in local features for image recognition intend to maximize the data variance, reduce quantitative errors, exploit the contextual information within each binary code, or utilize the label information, which all ignore the local manifold structure of the original data. The manifold structure actually plays a very important role in binary code learning, but constructing a similarity matrix for large-scale datasets involves a lot of computational and storage cost. The study attempts to learn a map, which can preserve the manifold structure between the original data and the learned binary codes for large-scale situations. To achieve this goal, we present a learning method using an anchor-based manifold binary pattern (AMBP) for finger vein recognition. Specifically, we first extract the pixel difference vectors (PDVs) in the local patches by calculating the differences between each pixel and its neighbors. Second, we construct an asymmetric graph, on which each data point can be a linear combination of its K-nearest neighbor anchors, and the anchors are randomly selected from the training samples. Third, a feature map is learned to project these PDVs into low-dimensional binary codes in an unsupervised manner, where (i) the quantization loss between the original real-valued vectors and learned binary codes is minimized and (ii) the manifold structure of the training data is maintained in the binary space. Additionally, the study fuses the discriminative binary descriptor and AMBP methods at the image representation level to further boost the performance of the recognition system. Finally, experiments using the MLA and PolyU databases show the effectiveness of our proposed methods.

Keywords

finger vein recognition feature learning local linear embedding fusion manifold learning anchor 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61472226, 61573219, 61703235), and Key Research and Development Project of Shandong Province (Grant No. 2018GGX101032). The authors would particularly like to thank the anonymous reviewers for their helpful suggestions.

References

  1. 1.
    Jain A K, Ross A, Prabhakar S. An introduction to biometric recognition. IEEE Trans Circ Syst Vid, 2004, 14: 4–20CrossRefGoogle Scholar
  2. 2.
    Yang J F, Shi Y H. Finger-vein ROI localization and vein ridge enhancement. Pattern Recogn Lett, 2012, 33: 1569–1579CrossRefGoogle Scholar
  3. 3.
    Yang J F, Zhang B, Shi Y H. Scattering removal for finger-vein image restoration. Sensors, 2012, 12: 3627–3640CrossRefGoogle Scholar
  4. 4.
    Lee E C, Park K R. Image restoration of skin scattering and optical blurring for finger vein recognition. Optics Lasers Eng, 2011, 49: 816–828CrossRefGoogle Scholar
  5. 5.
    Shin K, Park Y, Nguyen D, et al. Finger-vein image enhancement using a fuzzy-based fusion method with Gabor and retinex filtering. Sensors, 2014, 14: 3095–3129CrossRefGoogle Scholar
  6. 6.
    Lu Y, Xie S J, Yoon S, et al. Finger vein identication using polydirectional local line binary pattern. In: Proceedings of International Conference on ICT Convergence, 2013. 61–65Google Scholar
  7. 7.
    Yang G P, Xi X M, Yin Y L. Finger vein recognition based on a personalized best bit map. Sensors, 2012, 12: 1738–1757CrossRefGoogle Scholar
  8. 8.
    Rosdi B A, Shing C W, Suandi S A. Finger vein recognition using local line binary pattern. Sensors, 2011, 11: 11357–11371CrossRefGoogle Scholar
  9. 9.
    Xi X M, Yang G P, Yin Y L, et al. Finger vein recognition based on the hyperinformation feature. Opt Eng, 2014, 53: 013108CrossRefGoogle Scholar
  10. 10.
    Meng X J, Xi X M, Yang G P, et al. Finger vein recognition based on deformation information. Sci China Inf Sci, 2018, 61: 052103CrossRefGoogle Scholar
  11. 11.
    Lee E C, Jung H, Kim D. New finger biometric method using near infrared imaging. Sensors, 2011, 11: 2319–2333CrossRefGoogle Scholar
  12. 12.
    Yang L, Yang G P, Yin Y L, et al. Finger vein recognition with anatomy structure analysis. IEEE Trans Circ Syst Video Technol, 2018, 28: 1892–1905CrossRefGoogle Scholar
  13. 13.
    Deng W L, Hu J N, Guo J. Compressive binary patterns: designing a robust binary face descriptor with random-field eigenfilters. IEEE Trans Pattern Anal Mach Intell, 2019, 41: 758–767CrossRefGoogle Scholar
  14. 14.
    Lu J W, Liong V E, Zhou X Z, et al. Learning compact binary face descriptor for face recognition. IEEE Trans Pattern Anal Mach Intell, 2015, 37: 2041–2056CrossRefGoogle Scholar
  15. 15.
    Lu J W, Liong V E, Zhou J. Simultaneous local binary feature learning and encoding for homogeneous and heterogeneous face recognition. IEEE Trans Pattern Anal Mach Intell, 2018, 40: 1979–1993CrossRefGoogle Scholar
  16. 16.
    Duan Y Q, Lu J W, Feng J J, et al. Context-aware local binary feature learning for face recognition. IEEE Trans Pattern Anal Mach Intell, 2018, 40: 1139–1153CrossRefGoogle Scholar
  17. 17.
    Liu H Y, Yang L, Yang G P, et al. Discriminative binary descriptor for finger vein recognition. IEEE Access, 2018, 6: 5795–5804CrossRefGoogle Scholar
  18. 18.
    Weiss Y, Torralba A, Fergus R. Spectral hashing. In: Proceedings of Advances in Neural Information Processing Systems, 2008. 1753–1760Google Scholar
  19. 19.
    Liu W, Wang J, Sanjiv K, et al. Hashing with graphs. In: Proceedings of the 28th International Conference on Machine Learning (ICML), 2011Google Scholar
  20. 20.
    Ji R R, Liu H, Cao L J, et al. Toward optimal manifold hashing via discrete locally linear embedding. IEEE Trans Image Process, 2017, 26: 5411–5420MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Irie G, Li Z G, Wu X M, et al. Locally linear hashing for extracting non-linear manifolds. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2014. 2123–2130Google Scholar
  22. 22.
    Liu F, Yin Y L, Yang G P, et al. Finger vein recognition with superpixel-based features. In: Proceedings of IEEE International Joint Conference on Biometrics, 2014CrossRefGoogle Scholar
  23. 23.
    Zhou L Z, Yang G P, Yin Y L, et al. Finger vein recognition based on stable and discriminative superpixels. Int J Patt Recogn Artif Intell, 2016, 30: 1650015CrossRefGoogle Scholar
  24. 24.
    Dong L M, Yang G P, Yin Y L, et al. Finger vein verification based on a personalized best patches map. In: Proceedings of International Joint Conference Biometrics (IJCB), 2014CrossRefGoogle Scholar
  25. 25.
    Yu C B, Qin H F, Cui Y Z, et al. Finger-vein image recognition combining modified Hausdorff distance with minutiae feature matching. Interdiscip Sci Comput Life Sci, 2009, 1: 280–289CrossRefGoogle Scholar
  26. 26.
    Lee E C, Lee H C, Park K R. Finger vein recognition using minutia-based alignment and local binary pattern-based feature extraction. Int J Imag Syst Technol, 2009, 19: 179–186CrossRefGoogle Scholar
  27. 27.
    Kumar A, Zhou Y B. Human identification using finger images. IEEE Trans Image Process, 2012, 21: 2228–2244MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Song W, Kim T, Kim H C, et al. A finger-vein verification system using mean curvature. Pattern Recogn Lett, 2011, 32: 1541–1547CrossRefGoogle Scholar
  29. 29.
    Miura N, Nagasaka A, Miyatake T. Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Machine Vision Appl, 2004, 15: 194–203CrossRefGoogle Scholar
  30. 30.
    Lee H C, Kang B J, Lee E C, et al. Finger vein recognition using weighted local binary pattern code based on a support vector machine. J Zhejiang Univ Sci C, 2010, 11: 514–524CrossRefGoogle Scholar
  31. 31.
    Wu J D, Liu C T. Finger-vein pattern identification using principal component analysis and the neural network technique. Expert Syst Appl, 2011, 38: 5423–5427CrossRefGoogle Scholar
  32. 32.
    Yang G P, Xi X M, Yin Y L. Finger vein recognition based on (2D)2 PCA and metric learning. J Biomed Biotech, 2012, 2012: 1–9Google Scholar
  33. 33.
    Guan F X, Wang K J, Liu J Y, et al. Bi-direction weighted (2D)2 PCA with eigenvalue normalization one for finger vein recognition. Pattern Recogn Art Intell, 2011, 24: 417–424Google Scholar
  34. 34.
    Li Y Y, Lu R Q. Locality preserving projection on SPD matrix Lie group: algorithm and analysis. Sci China Inf Sci, 2018, 61: 092104MathSciNetCrossRefGoogle Scholar
  35. 35.
    Roweis S T. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290: 2323–2326CrossRefGoogle Scholar
  36. 36.
    Luxburg U V. A tutorial on spectral clustering. Stat Comput, 2007, 17: 395–416MathSciNetCrossRefGoogle Scholar
  37. 37.
    Elhamifar E, Vidal R. Sparse subspace clustering. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009. 2790–2797Google Scholar
  38. 38.
    Goh A, Vidal R. Segmenting motions of different types by unsupervised manifold clustering. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2007CrossRefGoogle Scholar
  39. 39.
    Eldar Y C, Mishali M. Robust recovery of signals from a structured union of subspaces. IEEE Trans Inf Theory, 2009, 55: 5302–5316MathSciNetCrossRefzbMATHGoogle Scholar
  40. 40.
    Liu G C, Lin Z C, Yu Y. Robust subspace segmentation by low-rank representation. In: Proceedings of the 27th International Conference on Machine Learning (ICML), 2010. 663–670Google Scholar
  41. 41.
    Liu W, Mu C, Kumar S, et al. Discrete graph hashing. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, 2014. 3419–3427Google Scholar
  42. 42.
    Wright J, Yang A, Ganesh A, et al. Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell, 2008, 31: 210–227CrossRefGoogle Scholar
  43. 43.
    Cai D, Chen X L. Large scale spectral clustering via landmark-based sparse representation. IEEE Trans Cybern, 2015, 45: 1669–1680CrossRefGoogle Scholar
  44. 44.
    Nie F P, Zhu W, Li X L. Unsupervised large graph embedding. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, 2017. 2422–2428Google Scholar
  45. 45.
    Wen Z W, Yin W T. A feasible method for optimization with orthogonality constraints. Math Program, 2013, 142: 397–434MathSciNetCrossRefzbMATHGoogle Scholar
  46. 46.
    Zhou Z H. Ensemble Methods: Foundations and Algorithms. Boca Raton: Chapman and Hall/CRC, 2012CrossRefGoogle Scholar
  47. 47.
    Yin Y L, Liu L L, Sun X W. Sdumla-hmt: a multimodal biometric database. In: Proceedings of Chinese Conference on Biometric Recognition, 2011. 260–268Google Scholar
  48. 48.
    Yang L, Yang G P, Yin Y L, et al. Sliding window-based region of interest extraction for finger vein images. Sensors, 2013, 13: 3799–3815CrossRefGoogle Scholar
  49. 49.
    Meng X J, Yang G P, Yin Y L, et al. Finger vein recognition based on local directional code. Sensors, 2012, 12: 14937–14952CrossRefGoogle Scholar
  50. 50.
    Xi X M, Yang L, Yin Y L. Learning discriminative binary codes for finger vein recognition. Pattern Recogn, 2017, 66: 26–33CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Haiying Liu
    • 1
  • Gongping Yang
    • 1
    Email author
  • Lu Yang
    • 2
  • Kun Su
    • 1
  • Yilong Yin
    • 1
  1. 1.School of SoftwareShandong UniversityJinanChina
  2. 2.School of Computer Science and TechnologyShandong University of Finance and EconomicsJinanChina

Personalised recommendations