Soft Computing

, Volume 22, Issue 7, pp 2257–2265 | Cite as

Finger vein secure biometric template generation based on deep learning

  • Yi Liu
  • Jie Ling
  • Zhusong Liu
  • Jian Shen
  • Chongzhi Gao
Methodologies and Application

Abstract

Leakage of unprotected biometric authentication data has become a high-risk threat for many applications. Lots of researchers are investigating and designing novel authentication schemes to prevent such attacks. However, the biggest challenge is how to protect biometric data while keeping the practical performance of identity verification systems. For the sake of tackling this problem, this paper presents a novel finger vein recognition algorithm by using secure biometric template scheme based on deep learning and random projections, named FVR-DLRP. FVR-DLRP preserves the core biometric information even with the user’s password cracked, whereas the original biometric information is still safe. The results of experiment show that the algorithm FVR-DLRP can maintain the accuracy of biometric identification while enhancing the uncertainty of the transformation, which provides better protection for biometric authentication.

Keywords

Secure biometric template Random projection Deep belief network 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (61572144), the Natural Science Foundation of Guangdong (2014A030313517), the Science and Technology Planning Project of Guangdong Province (2015B010129015, 2013B040500009), the Zhejiang Sicence Fund No. LY16F020016 and the Innovation Team Project of Guangdong Universities (No 2015KCXTD014).

Compliance with ethical standards

Conflict of interest

Yi Liu, Jie Lin, Zhusong Liu, Jian Shen and Chongzhi Gao all declare that they have no conflict of interest.

References

  1. Ang R, Safavi-Naini R, Mcaven L (2005) Cancelable key-based fingerprint templates. In: Proceedings of the Australasian conference information security and privacy (ACISP 2005), Brisbane, Australia, July 4–6, 2005, pp 242–252Google Scholar
  2. Chen C, Wu Z, Li P, Zhang J, Wang Y, Li H (2015) A finger vein recognition algorithm using feature block fusion and depth neural network. In: International symposium on intelligence computation and applications, Springer, Berlin, pp 572–583Google Scholar
  3. Gu B, Sheng VS, Tay KY, Romano W, Li S (2014) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416MathSciNetCrossRefGoogle Scholar
  4. Hinton GE (2010) A practical guide to training restricted Boltzmann machines. Momentum 9(1):599–619Google Scholar
  5. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554MathSciNetCrossRefMATHGoogle Scholar
  6. Jain AK, Ross A, Uludag U (2005) Biometric template security: Challenges and solutions. In: 2005 13th European signal processing conference. IEEE, pp 1–4Google Scholar
  7. Kong A, Zhang D, Kamel M (2008) Three measures for secure palmprint identification. Pattern Recogn 41(4):1329–1337CrossRefGoogle Scholar
  8. Li X, Guo S, Gao F, Li Y (2007) Vein pattern recognitions by moment invariants. In: The international conference on bioinformatics and biomedical engineering, pp 612–615Google Scholar
  9. Li J, Chen X, Li M, Li J, Lee PPC, Lou W (2013) Secure deduplication with efficient and reliable convergent key management. IEEE Trans Parallel Distrib Syst 25(6):1615–1625CrossRefGoogle Scholar
  10. Li J, Li X, Yang B, Sun X (2015a) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518CrossRefGoogle Scholar
  11. Li J, Li YK, Chen X, Lee P, Lou W (2015b) A hybrid cloud approach for secure authorized deduplication. IEEE Trans Parallel Distrib Syst 26(5):1206–1216CrossRefGoogle Scholar
  12. Lim MH, Teoh ABJ, Toh KA (2012) An efficient dynamic reliability-dependent bit allocation for biometric discretization. Pattern Recognit 45(5):1960–1971CrossRefGoogle Scholar
  13. Liu Z, Yin Y, Wang H, Song S, Li Q (2010) Finger vein recognition with manifold learning. J Netw Comput Appl 33(3):275–282CrossRefGoogle Scholar
  14. Maio D, Nanni L (2005) Multihashing, human authentication featuring biometrics data and tokenized random number: a case study fvc2004. Neurocomputing 69(1):242–249CrossRefGoogle Scholar
  15. Maiorana E (2010) Biometric cryptosystem using function based on-line signature recognition. Expert Syst Appl 37(4):3454–3461CrossRefGoogle Scholar
  16. Nanni L, Lumini A (2008) Random subspace for an improved biohashing for face authentication. Pattern Recognit Lett 29(3):295–300CrossRefMATHGoogle Scholar
  17. Pankanti S, Jain A, Hong L (2000) Biometrics: promising frontiers for emerging identification market. Comm ACM 43:91–98Google Scholar
  18. Quan F, Fei S, Anni C, Feifei Z (2008) Cracking cancelable fingerprint template of ratha. In: International symposium on computer science and computational technology, pp 572–575Google Scholar
  19. Ratha NK, Connell JH, Bolle RM (2001) Enhancing security and privacy in biometrics-based authentication systems. IBM Syst J 40(3):614–634CrossRefGoogle Scholar
  20. Ratha NK, Chikkerur S, Connell JH, Bolle RM (2007) Generating cancelable fingerprint templates. IEEE Trans Pattern Anal Mach Intell 29(4):561–572CrossRefGoogle Scholar
  21. Rua EA, Maiorana E, Castro JLA, Campisi P (2012) Biometric template protection using universal background models: an application to online signature. IEEE Trans Inf Forensics Secur 7(1):269–282CrossRefGoogle Scholar
  22. Shen J, Tan H, Wang J, Wang J, Lee S (2015) A novel routing protocol providing good transmission reliability in underwater sensor networks. J Internet Technol 16(1):171–178Google Scholar
  23. Uludag U, Pankanti S, Prabhakar S, Jain AK (2004) Biometric cryptosystems: issues and challenges. Proc IEEE 92(6):948–960CrossRefGoogle Scholar
  24. Wang KJ (2007) Finger vein recognition based on wavelet moment fused with PCA transform. Pattern Recognit Artif Intell 20(5):692–697Google Scholar
  25. Wang J, Ma H, Tang Q, Li J, Zhu H, Ma S, Chen X (2013) Efficient verifiable fuzzy keyword search over encrypted data in cloud computing. Comput Sci Inf Syst 10(2):667–684CrossRefGoogle Scholar
  26. Wen X, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295:395–406CrossRefGoogle Scholar
  27. Wu Z, Liang B, You L, Jian Z, Li J (2016a) High-dimension space projection-based biometric encryption for fingerprint with fuzzy minutia. Soft Comput 20(12):4907–4918CrossRefGoogle Scholar
  28. Wu Z, Yu Z, Yuan J, Zhang J (2016b) A twice face recognition algorithm. Soft Comput 20(3):1007–1019CrossRefGoogle Scholar
  29. Wu Z, Yuan J, Zhang J, Huang H (2016c) A hierarchical face recognition algorithm based on humanoid nonlinear least-squares computation. J Ambient Intell Human Comput 7(2):229–238CrossRefGoogle Scholar
  30. Yan F, Tan Y, Zhang Q, Wu F, Cheng Z, Zheng J (2016) An effective raid data layout for object-based de-duplication backup system. Chin J Electron 25(5):832–840CrossRefGoogle Scholar
  31. Zheng Y, Byeungwoo J, Xu D, Wu QMJ, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy c-means algorithm. J Intell Fuzzy Syst 28(2):4024–4028Google Scholar
  32. Zhu R, Ya Tan, Zhang Q, Fei W, Zheng J, Yuan X (2016a) Determining image base of firmware files for arm devices. IEICE Trans Inf Syst 99(2):351–359CrossRefGoogle Scholar
  33. Zhu R, Ya Tan, Zhang Q, Li Y, Zheng J (2016b) Determining image base of firmware for arm devices by matching literal pools. Dig Investig 16:19–28CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Yi Liu
    • 1
  • Jie Ling
    • 1
  • Zhusong Liu
    • 1
  • Jian Shen
    • 2
  • Chongzhi Gao
    • 3
  1. 1.School of Computer Science and TechnologyGuangdong University of TechnologyGuangzhouChina
  2. 2.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina
  3. 3.School of Computer ScienceGuangzhou UniversityGuangzhouChina

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