Cancelable Biometrics with Perfect Secrecy for Correlation-Based Matching

  • Shinji Hirata
  • Kenta Takahashi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


In this paper, we propose a novel method of Cancelable Biometrics for correlation-based matching. The biometric image is transformed by Number Theoretic Transform (Fourier-like transform over a finite field), and then the transformed data is masked with a random filter. By applying a particular kind of masking technique, the correlation between the registered image and the input matching image can be computed in masked domain (i.e., encrypted domain) without knowing the original images. And we proved theoretically that in our proposed method the masked version does not leak any information of the original image, in other words, our proposed method has perfect secrecy. Additionally, we applied our proposed method to finger-vein pattern verification and experimentally obtained very high verification performance.


Original Image Registered Image Biometric Feature Biometric Template Masking Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Ratha, N.K., Connell, J.H., Bolle, R.M.: Enhancing security and privacy in biometric-based authentication systems. IBM System Journal 40(3) (2001)Google Scholar
  2. 2.
    Savvides, M., Vijayakumar, B.V.K., Khosla, P.K.: Cancelable Biometric Filters for Face Recognition. In: 17th International Conference on Pattern Recognition (ICPR 2004), vol. 3, pp. 922–925 (2004)Google Scholar
  3. 3.
    Connie, T., Teoh, A., Goh, M., Ngo, D.: PalmHashing: a novel approach for cancelable biometrics. Information Processing Letters 93(1), 1–5 (2005)Google Scholar
  4. 4.
    Ratha, N.K., Connell, J.H., Bolle, R.M., Chikkerur, S.: Cancelable Biometrics: A Case Study in Fingerprints. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 4, pp. 370–373 (2006)Google Scholar
  5. 5.
    Agarwal, R.C., Burrus, C.S.: Number theoretic transforms to implement fast digital convolution. Proc. IEEE 63(4), 550–560 (1975)Google Scholar
  6. 6.
    Rosenfeld, A., Kak, A.C.: Digital Picture Processing, 2nd edn., vol. 2. Academic Press, London (1982)Google Scholar
  7. 7.
    Reed, I.S., Truong, T.K., Kwoh, Y.S., Hall, E.L.: Image Processing by Transforms Over a Finte Field. IEEE Transactions on Computers C-26(9), 874–881 (1977)Google Scholar
  8. 8.
    Buchmann, J.A.: Introduction to Cryptography, 2nd edn. Springer, Heidelberg (2004)Google Scholar
  9. 9.
    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 and Applications 15(4), 194–203 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Shinji Hirata
    • 1
  • Kenta Takahashi
    • 1
  1. 1.Systems Development LaboratoryHitachi Ltd.Kanagawa-kenJapan

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