Finger vein recognition based on deformation information

  • Xianjing Meng
  • Xiaoming Xi
  • Gongping Yang
  • Yilong YinEmail author
Research Paper


The measurement of the vessel pattern in fingers is a superior method for identifying individuals owing to its convenience and the security it offers. We introduce in this paper a new perspective to accomplish finger vein recognition. This method, which regards deformations as discriminative information, is distinct from existing methods that attempt to prevent the influence of deformations. The proposed technique is based on the observation that regular deformation, which corresponds to a posture change, can only exist in genuine vein patterns. In terms of methodology, we incorporate optimized matching to generate pixelbased 2D displacements that correspond to deformations. The texture of uniformity extracted from the displacement fields is taken as the final matching score. Evaluated on two publicly available databases, PolyU and SDU-MLA, extensive experiments demonstrated that the discriminability of the new feature derived from deformations is preferable. The equal error rate (EER) achieved is the lowest compared to that of state-of-the-art techniques.


finger vein recognition deformations optimized matching displacement texture of uniformity 



The work was supported by National Science Foundation of China (Grant Nos. 61573219, 61472226), NSFC Joint Fund with Guangdong under Key Project (Grant No. U1201258), Natural Science Foundation for the Youth of Shandong Province (Grant No. ZR2016FQ18), and Fostering Project of Dominant Discipline and Talent Team of Shandong Province Higher Education Institutions.


  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.
    Ross A A, Nandakumar K, Jain A K. Handbook of Multibiometrics. Berlin: Springer Science & Business Media, 2006. 6Google Scholar
  3. 3.
    Yanagawa T, Aoki S, Ohyama T. Human finger vein images are diverse and its patterns are useful for personal identification. MHF Prepr Ser, 2007, 12: 1–7zbMATHGoogle Scholar
  4. 4.
    Hashimoto J. Finger vein authentication technology and its future. In: Proceedings of 2006 Symposium on VLSI Circuits, Digest of Technical Papers, Honolulu, 2006. 5–8Google Scholar
  5. 5.
    Liu Z, Yin Y, Wang H, et al. Finger vein recognition with manifold learning. J Netw Comput Appl, 2010, 33: 275–282CrossRefGoogle Scholar
  6. 6.
    Wu J D, Ye S H. Driver identification using finger-vein patterns with radon transform and neural network. Expert Syst Appl, 2009, 36: 5793–5799CrossRefGoogle Scholar
  7. 7.
    Kiyomizu H, Miura N, Miyatake T, et al. Finger vein authentication device. US Patent 8811689, 2014-08-19Google Scholar
  8. 8.
    Sato H. Finger vein authentication apparatus and finger vein authentication method. US Patent 8229179, 2012-07-24Google Scholar
  9. 9.
    Lee E C, Jung H, Kim D. New finger biometric method using near infrared imaging. Sensors, 2011, 11: 2319–2333CrossRefGoogle Scholar
  10. 10.
    Meng X, Yang G, Yin Y, et al. Finger vein recognition based on local directional code. Sensors, 2010, 12: 14937–14952CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Miura N, Nagasaka A, Miyatake T. Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Mach Vision Appl, 2004, 15: 194–203CrossRefGoogle Scholar
  13. 13.
    Kumar A, Zhou Y. Human identification using finger images. IEEE Trans Biomed Eng, 2012, 21: 2228–2244MathSciNetGoogle Scholar
  14. 14.
    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
  15. 15.
    Liu F, Yang G, Yin Y, et al. Singular value decomposition based minutiae matching method for finger vein recognition. Neurocomputing, 2014, 145: 75–89CrossRefGoogle Scholar
  16. 16.
    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
  17. 17.
    Yang G, Xi X, Yin Y. Finger vein recognition based on (2d)2pca and metric learning. J Biomed Biotech, 2012, 2012: 324249Google Scholar
  18. 18.
    Guan F, Wang K, Liu J, et al. Bi-direction weighted (2d)2pca with eigenvalue normalization one forefinger vein recognition. Pattern Recogn Art Intell, 2011, 24: 417–424Google Scholar
  19. 19.
    Wu J D, Liu C T. Finger-vein pattern identification using svm and neural network technique. Expert Syst Appl, 2011, 38: 14284–14289Google Scholar
  20. 20.
    Yang L, Yang G, Yin Y, et al. A survey of finger vein recognition. In: Proceedings of Chinese Conference on Biometric Recognition. Berlin: Springer, 2014. 234–243Google Scholar
  21. 21.
    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 Tech, 2009, 19: 179–186CrossRefGoogle Scholar
  22. 22.
    Lee E C, Park K R. Image restoration of skin scattering and optical blurring for finger vein recognition. Opt Laser Eng, 2011, 49: 816–828CrossRefGoogle Scholar
  23. 23.
    Mulyono D, Jinn H S. A study of finger vein biometric for personal identification. In: Proceedings of International Symposium on Biometrics and Security Technologies, Islamabad, 2008. 1–8Google Scholar
  24. 24.
    Qin H, Qin L, Xue L, et al. Finger-vein verification based on multi-features fusion. Sensors, 2013, 13: 15048–15067CrossRefGoogle Scholar
  25. 25.
    Yang J, Shi Y. Finger–vein roi localization and vein ridge enhancement. Pattern Recogn Lett, 2012, 33: 1569–1579CrossRefGoogle Scholar
  26. 26.
    Lu Y, Xie S J, Yoon S, et al. Robust finger vein roi localization based on flexible segmentation. Sensors, 2013, 13: 14339–14366CrossRefGoogle Scholar
  27. 27.
    Yang L, Yang G, Yin Y, et al. Sliding window-based region of interest extraction for finger vein images. Sensors, 2013, 13: 3799–3815CrossRefGoogle Scholar
  28. 28.
    Miura N, Nagasaka A, Miyatake T. Extraction of finger-vein patterns using maximum curvature points in image profiles. IEICE Trans Inf Syst, 2007, 90: 1185–1194CrossRefGoogle Scholar
  29. 29.
    Ojala T, Pietikäinen M, Mäenpää T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell, 2002, 24: 971–987CrossRefzbMATHGoogle Scholar
  30. 30.
    Pang S, Yin Y, Yang G, et al. Rotation invariant finger vein recognition. In: Proceedings of Chinese Conference on Biometric Recognition. Berlin: Springer, 2012. 151–156CrossRefGoogle Scholar
  31. 31.
    Rosdi B A, Shing C W, Suandi S A. Finger vein recognition using local line binary pattern. Sensors, 2011, 11: 11357–11371CrossRefGoogle Scholar
  32. 32.
    Zhang B, Gao Y, Zhao S, et al. Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans Image Process, 2010, 19: 533–544MathSciNetCrossRefGoogle Scholar
  33. 33.
    Xi X, Yang G, Yin Y, et al. Finger vein recognition with personalized feature selection. Sensors, 2013, 13: 11243–11259CrossRefGoogle Scholar
  34. 34.
    Liu C, Yuen J, Torralba A, et al. Sift flow: dense correspondence across different scenes. In: Proceedings of European Conference on Computer Vision. Berlin: Springer, 2008. 28–42Google Scholar
  35. 35.
    Lowe D G. Distinctive image features from scale-invariant keypoints. Int J Comput Vision, 2004, 60: 91–110CrossRefGoogle Scholar
  36. 36.
    Gonzalez R C, Woods R E, Eddins E L. Digital Image Processing Using MATLAB. Princeton: Pearson Education Inc., 2004Google Scholar
  37. 37.
    Yin Y, Liu L, Sun X. Sdumla-hmt: a multimodal biometric database. In: Proceedings of Chinese Conference on Biometric Recognition. Berlin: Springer, 2011. 260–268CrossRefGoogle Scholar
  38. 38.
    Si X, Feng J, Zhou J, et al. Detection and rectification of distorted fingerprints. IEEE Trans Pattern Anal Mach Intell, 2015, 37: 555–568CrossRefGoogle Scholar
  39. 39.
    Kim J, Choi J, Yi J, et al. Effective representation using ica for face recognition robust to local distortion and partial occlusion. IEEE Trans Pattern Anal Mach Intell, 2005, 27: 1977–1981CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Xianjing Meng
    • 1
  • Xiaoming Xi
    • 1
  • Gongping Yang
    • 2
  • Yilong Yin
    • 2
    Email author
  1. 1.School of Computer Science and TechnologyShandong University of Finance and EconomicsJinanChina
  2. 2.School of Computer Science and TechnologyShandong UniversityJinanChina

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