Cancelable Biometrics Using Hadamard Transform and Friendly Random Projections

  • Harkeerat KaurEmail author
  • Pritee Khanna
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 460)


Biometrics based authentication increases robustness and security of a system, but at the same time biometric data of a user is subjected to various security and privacy issues. Biometric data is permanently associated to a user and cannot be revoked or changed unlike conventional PINs/passwords in case of thefts. Cancelable biometrics is a recent approach which aims to provide high security and privacy to biometric templates as well as imparting them with the ability to be canceled like passwords. The work proposes a novel cancelable biometric template protection algorithm based on Hadamard transform and friendly random projections using Achlioptas matrices followed by a one way modulus hashing. The approach is tested on face and palmprint biometric modalities. A thorough analysis is performed to study performance, non-invertibility, and distinctiveness of the proposed approach which reveals that the generated templates are non-invertible, easy to revoke, and also deliver good performance.


Cancelable biometrics Hadamard transform Random projections Non-invertible 


  1. 1.
    Ratha, N.K., Connell, J.H., Bolle, R.M.: Enhancing security and privacy in biometrics-based authentication systems. IBM systems Journal 40 (2001) 614–634CrossRefGoogle Scholar
  2. 2.
    Lacharme, P., Cherrier, E., Rosenberger, C.: Preimage attack on biohashing. In: International Conference on Security and Cryptography (SECRYPT). (2013)Google Scholar
  3. 3.
    Sutcu, Y., Sencar, H.T., Memon, N.: A secure biometric authentication scheme based on robust hashing. In: Proceedings of the 7th workshop on Multimedia and security, ACM (2005) 111–116Google Scholar
  4. 4.
    Teoh, A.B.J., Ngo, D.C.L.: Biophasor: Token supplemented cancellable biometrics. In: Control, Automation, Robotics and Vision, 2006. ICARCV’06. 9th International Conference on, IEEE (2006) 1–5Google Scholar
  5. 5.
    Teoh, A., Yuang, C.T.: Cancelable biometrics realization with multispace random projections. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 37 (2007) 1096–1106CrossRefGoogle Scholar
  6. 6.
    Lumini, A., Nanni, L.: An improved biohashing for human authentication. Pattern recognition 40 (2007) 1057–1065CrossRefzbMATHGoogle Scholar
  7. 7.
    Ratha, N., Connell, J., Bolle, R.M., Chikkerur, S.: Cancelable biometrics: A case study in fingerprints. In: Pattern Recognition, 2006. ICPR 2006. 18th International Conference on. Volume 4., IEEE (2006) 370–373Google Scholar
  8. 8.
    Tulyakov, S., Farooq, F., Govindaraju, V.: Symmetric hash functions for fingerprint minutiae. In: Pattern Recognition and Image Analysis. Springer (2005) 30–38Google Scholar
  9. 9.
    Ang, R., Safavi-Naini, R., McAven, L.: Cancelable key-based fingerprint templates. In: Information Security and Privacy, Springer (2005) 242–252Google Scholar
  10. 10.
    Boult, T.E., Scheirer, W.J., Woodworth, R.: Revocable fingerprint biotokens: Accuracy and security analysis. In: Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on, IEEE (2007) 1–8Google Scholar
  11. 11.
    Farooq, F., Bolle, R.M., Jea, T.Y., Ratha, N.: Anonymous and revocable fingerprint recognition. In: Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on, IEEE (2007) 1–7Google Scholar
  12. 12.
    Dasgupta, S., Gupta, A.: An elementary proof of the johnson-lindenstrauss lemma. International Computer Science Institute, Technical Report (1999) 99–006Google Scholar
  13. 13.
    Matoušek, J.: On variants of the johnson–lindenstrauss lemma. Random Structures & Algorithms 33 (2008) 142–156MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the thirtieth annual ACM symposium on Theory of computing, ACM (1998) 604–613Google Scholar
  15. 15.
    Dasgupta, S.: Learning mixtures of gaussians. In: Foundations of Computer Science, 1999. 40th Annual Symposium on, IEEE (1999) 634–644Google Scholar
  16. 16.
    Achlioptas, D.: Database-friendly random projections. In: Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, ACM (2001) 274–281Google Scholar
  17. 17.
    Bingham, E., Mannila, H.: Random projection in dimensionality reduction: applications to image and text data. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, ACM (2001) 245–250Google Scholar
  18. 18.
    Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. Neural Networks, IEEE Transactions on 13 (2002) 1450–1464CrossRefGoogle Scholar
  19. 19.
    Connie, T., Teoh, A., Goh, M., Ngo, D.: Palmprint recognition with pca and ica. In: Proc. Image and Vision Computing, New Zealand. (2003)Google Scholar
  20. 20.
    ORL face database: (AT&T Laboratories Cambridge)
  21. 21.
    Yale face database: (Center for computational Vision and Control at Yale University)
  22. 22.
    The Indian face database: (IIT Kanpur)
  23. 23.
    CASIA palmprint database: (Biometrics Ideal Test)
  24. 24.
    PolyU palmprint database: (The Hong Kong Polytechnic University)
  25. 25.
    Kekre, H., Sarode, T., Vig, R.: An effectual method for extraction of roi of palmprints. In: Communication, Information & Computing Technology (ICCICT), 2012 International Conference on, IEEE (2012) 1–5Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  1. 1.PDPM Indian Institute of Information TechnologyDesign and ManufacturingJabalpurIndia

Personalised recommendations