CCCV 2017: Computer Vision pp 375-386 | Cite as

Hierarchical Structure Construction Based on Hyper-sphere Granulation for Finger-Vein Recognition

Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (CCIS, volume 773)

Abstract

Recently, the finger-vein (FV) trait has attracted substantial attentions for personal recognition in biometric community, and some FV-based biometric systems have been well developed in real applications. However, the recognition efficiency improvement over a large-scale database remains a big practical problem. In this paper, we propose an efficient and powerful hierarchical model based on hyper-sphere granular computing (HsGrC) for saving recognition cost. For a given FV database, samples are first viewed as atomic granules for building a basic hyper-sphere granule set. Using HsGrC, several different granule sets with multi-granularities are then generated by hyper-sphere granulation. To build a hierarchical structure of granule sets with granularity variation, a new quotient space relationship is established considering recognition efficiency improvement. Experimental results over a large finger-vein image database demonstrate that the proposed hierarchical model performs very well in computing cost reduction as well as recognition accuracy improvement.

Keywords

Finger-vein recognition Granular computing Biometrics 
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References

  1. 1.
    Chen, J.: Research on granulation technologies and problem solving methods based on quotient space theory (2014)Google Scholar
  2. 2.
    Daugman, J.: How iris recognition works. IEEE Trans. Circ. Syst. Video Technol. 14(1), 21–30 (2004)CrossRefGoogle Scholar
  3. 3.
    Ding, Y., Zhuang, D., Wang, K.: A study of hand vein recognition method. In: Proceedings of the IEEE International Conference on Mechatronics and Automation, vol. 4, pp. 2106–2110 (2005)Google Scholar
  4. 4.
    Huang, D., Jia, W., Zhang, D.: Palmprint verification based on principal lines. Pattern Recogn. 41(4), 1316–1328 (2008)CrossRefGoogle Scholar
  5. 5.
    Jain, A., Flynn, P., Ross, A.: Handbook of Biometrics. Springer, New York (2007).  https://doi.org/10.1007/978-0-387-71041-9 Google Scholar
  6. 6.
    Kong, A., Zhang, D., Kamel, M.: Palmprint identification using feature-level fusion. Pattern Recogn. 39(3), 478–487 (2006)CrossRefMATHGoogle Scholar
  7. 7.
    Kono, M., Ueki, H., Umemura, S.: Near-infrared finger vein patterns for personal identification. Appl. Opt. 41(35), 7429–7436 (2002)CrossRefGoogle Scholar
  8. 8.
    Liu, H., Li, L., Wu, C.: Color image segmentation algorithms based on granular computing clustering. Int. J. Sig. Process. Image Process. Pattern Recogn. 7(1), 155–168 (2014)Google Scholar
  9. 9.
    Liu, H., Liu, C., Wu, C.: Granular computing classification algorithms based on distance measures between granules from the view of set. Comput. Intell. Neurosci. (2014)Google Scholar
  10. 10.
    Liu, H., Zhang, F., Wu, C., Huang, J.: Image superresolution reconstruction via granular computing clustering. Comput. Intell. Neurosci. 1(50) (2014)Google Scholar
  11. 11.
    Liu, Q.: Granular language and its deductive reasoning. Commun. Inst. Inf. Comput. Mach. 5(2), 63–66 (2002)Google Scholar
  12. 12.
    Liu, Q., Liu, Q.: Approximate reasoning based on granular computing in granular logic. In: International Conference on Machine Learning and Cybernetics, Hoboken, USA, vol. 3, pp. 1258–1262 (2002)Google Scholar
  13. 13.
    Maadooliat, M., Huang, J., Hu, J.: Integrating data transformation in principal components analysis. J. Comput. Graph. Stat. 24(1), 84–103 (2015)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Miura, N., Nagasaka, A.: Feature extraction of finger-vein pattern based on repeated line tracking and its application to personal identification. Mach. Vis. Appl. 15(4), 194–203 (2004)CrossRefGoogle Scholar
  15. 15.
    Miura, N., Nagasaka, A.: Extraction of finger-vein patterns using maximum curvature points in image profiles. IEICE - Trans. Inf. Syst. 90, 185–1194 (2007)Google Scholar
  16. 16.
    Ratha, N., Bolle, R.: Automatic Fingerprint Recognition Systems. Springer-Verlag New York, Inc., New York (2004).  https://doi.org/10.1007/b97425 CrossRefGoogle Scholar
  17. 17.
    Ross, A., Sunder, M.: Block based texture analysis for iris classification and matching. In: Computer Vision and Pattern Recognition Conference, pp. 30–37 (2010)Google Scholar
  18. 18.
    Tan, D., Yang, J., Shi, Y., Xu, C.: A hierarchal framework for finger-vein image classification. In: Asian Conference on Pattern Recognition, pp. 833–837 (2013)Google Scholar
  19. 19.
    Wang, G., Xu, J.: Granular computing with multiple granular layers for brain big data processing. Brain Info. 1, 1–10 (2014)CrossRefGoogle Scholar
  20. 20.
    Wechsler, H.: Reliable Face Recognition Methods - System Design, Implementation and Evaluation. Springer, Boston (2006).  https://doi.org/10.1007/978-0-387-38464-1 Google Scholar
  21. 21.
    Xie, G., Liu, J.: A review of the present studying state and prospect of granular computing. Software 32(3), 5–10 (2011)Google Scholar
  22. 22.
    Yang, J., Shi, Y., Yang, J.: Personal identification based on finger-vein features. Comput. Hum. Behav. 27(5), 1565–1570 (2010)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Zhang, D., Kong, W.K., You, J., Wong, M.: Online palmprint identification. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1041–1050 (2003)CrossRefGoogle Scholar
  24. 24.
    Zhang, L., Zhang, B.: The theory and application of problem solving (1990)Google Scholar
  25. 25.
    Zhang, L., Zhang, B.: Theory of fuzzy quotient space. J. Softw. 14, 770–776 (2003)MATHGoogle Scholar
  26. 26.
    Zhang, L., Zhang, B.: Fuzzy reasoning model under quotient space structure. Inf. Sci. 173(4), 353–364 (2005)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Jinfeng Yang
    • 1
  • Yuqing Yang
    • 2
  • Zhiyuan Liu
    • 1
  • Yihua Shi
    • 1
  1. 1.Tianjin Key Lab for Advanced Signal ProcessingCivil Aviation University of ChinaTianjinChina
  2. 2.Tianjin University of CommerceTianjinChina

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