Advertisement

Signal, Image and Video Processing

, Volume 9, Supplement 1, pp 99–109 | Cite as

A novel template protection scheme for multibiometrics based on fuzzy commitment and chaotic system

  • Ning Wang
  • Qiong Li
  • Ahmed A. Abd El-Latif
  • Jialiang Peng
  • Xuehu Yan
  • Xiamu Niu
Original Paper

Abstract

In recent years, biometrics template protection has been extensively studied and lots of schemes have been proposed. However, most of them have not considered the forgery, large difference of intra-class and the security of unimodal biometrics leakage. And there is no multibiometrics template scheme based on the fusion of dual iris, thermal and visible face images. In this paper, a novel multibiometrics template protection scheme based on fuzzy commitment and chaotic system, and the security analysis approach for unimodal biometrics leakage are proposed. Firstly, the thermal face images are captured to overcome the forgery. Then, the fuzzy commitment is generated from the corporation of error correcting code (ECC) and the fusion binary features. Additionally, the dual iris feature vectors are encrypted via the chaotic system, and the score level fusion based on Aczél-Alsina triangular-norm (AA T-norm) is implemented to acquire the final verification performance. Finally, the entropy of both mutlibiometrics and unimodal information leakage is analyzed to show the security of the proposed approach. The experimental tests are conducted on a virtual multibiometrics database, which merges the challenging CASIA-Iris-Thousand and the NVIE face database. The verification performance decreases from EER of \(3 \times 10^{-2}\) to \(1.163 \times 10^{-1}\) %, but the multibiometrics template security is enhanced from 80.53 to 167.80 bits based on BCH ECC (1,023, 123, 170).

Keywords

Template protection Multibiometrics Fuzzy commitment Chaotic system Entropy analysis 

List of symbols

ECC

Error correcting code

AA T-norm

Aczél-Alsina triangular-norm

FC

Fuzzy commitment

FV

Fuzzy vault;

OCML

One-way coupled map lattice

NVIE

Natural visible and infrared facial expression

EER

Equal error rate

FMR

False matching rate

FNMR

False non-matching rate

DET

Detection error tradeoff

GMR

Genuine matching rate

G-S

GMR-security

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant No.: 61100187), the Fundamental Research Funds for the Central Universities (Grant No.: HIT. NSRIF. 2013061.) and Ministry of Scientific Research (Egypt-Tunisia Cooperation Program, Code No: 4-13-A1).

References

  1. 1.
    Wang, N., Li, Q., El-Latif, A.A.A., Zhang, T., Niu, X.: Toward accurate localization and high recognition performance for noisy iris images. Multimedia Tools Appl. 71(3), 1411 –1430 (2014).  doi: 10.1007/s11042-012-1278-7
  2. 2.
    Venugopalan, S., Savvides, M.: How to generate spoofed irises from an iris code template. IEEE Trans. Inf. Foren. Sec. 6(2), 385–395 (2011)CrossRefGoogle Scholar
  3. 3.
    Mane, V.M., Jadhav, D.V.: Review of multimodal biometrics: applications, challenges and research areas. J. Biom. Bioinf. 3(5), 90–95 (2009)Google Scholar
  4. 4.
    Fu, B., Yang, S.X., Li, J., Hu, D.: Multibiometric cryptosystem: model structure and performance analysis. IEEE Trans. Inf. Forensics Secur. 4(4), 867–882 (2009)CrossRefGoogle Scholar
  5. 5.
    Nandakumar, K., Jain, A.K.: Multibiometric template security using fuzzy vault. Presented at the IEEE 2nd International Conference on Biometrics: Theory, Applications and Systems, Arlington, VA, United States (2008)Google Scholar
  6. 6.
    Ross, A.A., Nandakumar, K., Jain, A.K.: Handbook of Multibiometrics, Ch. 1. Springer, London (2006)Google Scholar
  7. 7.
    Wang, M., Wang, N., Yao, X.: Noisy iris segmentation with reflections removal using probable boundary edge detector. Appl. Mech. Mater. 236–237, 1116–1121 (2012)CrossRefGoogle Scholar
  8. 8.
    Wang, N., Li, Q., El-Latif, A.A.A., Peng, J., Niu, X.: An enhanced thermal face recognition method based on multiscale complex fusion for Gabor coefficients. Multimedia Tools Appl. doi: 10.1007/s11042-013-1551-4
  9. 9.
    Jain, A.K., Nandakumar, K., Nagar, A.: Biometric template security. EURASIP J. Adv. Sig. Pr. 2008(113), 1–20 (2008)CrossRefGoogle Scholar
  10. 10.
    Ang, R., Safavi-Naini, R., McAven, L.: Cancelable key-based fingerprint templates. Lect. Notes Comput. Sci. 3574, 242–252 (2005)CrossRefGoogle Scholar
  11. 11.
    Moon, D., Gil, Y.H., Ahn, D., Pan, S.B., Chung, Y., Park, C.H.: Fingerprint-based authentication for USB token systems Chee Hang park. Lect. Notes Comput. Sci. 2908, 355–364 (2003) Google Scholar
  12. 12.
    Souta, C., Boberge, D., Stoianov, A., Gilroy, R., Kumar, B.V.K.V.: ICSA Guide to Cryptography, Ch. 22. McGraw-Hill Companies, New York (1998)Google Scholar
  13. 13.
    Juels, A., Wattenberg, M.: Fuzzy commitment scheme. In: Proceedings of ACM Conference Computer and Communications Security, Singapore, Singapore, pp. 28–36 (1999)Google Scholar
  14. 14.
    Juels, A., Sudan, M.: A fuzzy vault scheme. Des. Codes Cryptogr. 38(2), 237–257 (2006)zbMATHMathSciNetCrossRefGoogle Scholar
  15. 15.
    Dodis, Y., Ostrovsky, R., Reyzin, L., Smith, A.: Fuzzy extractors: how to generate strong keys from biometrics and other noisy data. SIAM J. Comput. 38(1), 97–139 (2008)zbMATHMathSciNetCrossRefGoogle Scholar
  16. 16.
    Yanikoglu, B., Kholmatov, A.: Combining multiple biometrics to protect privacy. In: Proceedings of 17th International Conference on Pattern Recognition Workshop on Biometrics: Challenges Arising from Theory to Practice, Cambridge, England, pp. 1–4 (2004)Google Scholar
  17. 17.
    Sutcu, Y., Li, Q., Memon, N.: Secure biometric templates from fingerprint-face features. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Minneapolis, United States, pp. 17–22 (2007)Google Scholar
  18. 18.
    Kanade, S., Petrovska-Delacretaz, D., Dorizzi, B.: Obtaining cryptographic keys using feature level fusion of iris and face biometrics for secure user authentication. Presented at the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, San Francisco, CA, United States, pp. 138–145 (2010)Google Scholar
  19. 19.
    Rathgeb, C., Uhl, A., Wild, P.: Reliability-balanced feature level fusion for fuzzy commitment scheme. Presented at the 2011 International Joint Conference on Biometrics, Washington, DC, United States, pp. 1–7 (2011)Google Scholar
  20. 20.
    Nagar, A., Nandakumar, K., Jain, A.: Multibiometric cryptosystems based on feature-level fusion. IEEE Trans. Inf. Forensics (2) 7(1), 255–268 (2012)CrossRefGoogle Scholar
  21. 21.
    Abd El-Latif, A.A., Niu, X., Amin, M.: A new image cipher in time and frequency domains. Opt. Commun. 285(21–22), 4241–4251 (2012)Google Scholar
  22. 22.
    Wang, N., Li, Q., El-Latif, A.A.A., Yan, X., Niu, X.: A novel hybrid multibiometrics based on the fusion of dual iris, visible and thermal face images. Presented at the International Symposium on Biometrics and Security Technologies, pp. 1–6 (2013)Google Scholar
  23. 23.
    János, A., Alsina, C.: Characterizations of some classes of quasilinear functions with applications to triangular norms and to synthesizing judgements. Aequationes Math. 25(1), 313–315 (1982)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Shen, W., Surette, M., Khanna, R.: Evaluation of automated biometrics-based identification and verification systems. Proc. IEEE 85(9), 1464–1467 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Ning Wang
    • 1
  • Qiong Li
    • 1
    • 2
  • Ahmed A. Abd El-Latif
    • 1
    • 3
  • Jialiang Peng
    • 1
    • 4
  • Xuehu Yan
    • 1
  • Xiamu Niu
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.Science Zone of Harbin Institute of TechnologyHarbinChina
  3. 3.Mathematics Department, Faculty of ScienceMenoufia UniversityMenufiaEgypt
  4. 4.Information and Network Administration CenterHeilongjiang UniversityHarbinChina

Personalised recommendations