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Random Multispace Quantization as an Analytic Mechanism for BioHashing of Biometric and Random Identity Inputs
December 2006 (vol. 28 no. 12)
pp. 1892-1901
Biometric analysis for identity verification is becoming a widespread reality. Such implementations necessitate large-scale capture and storage of biometric data, which raises serious issues in terms of data privacy and (if such data is compromised) identity theft. These problems stem from the essential permanence of biometric data, which (unlike secret passwords or physical tokens) cannot be refreshed or reissued if compromised. Our previously presented biometric-hash framework prescribes the integration of external (password or token-derived) randomness with user-specific biometrics, resulting in bitstring outputs with security characteristics (i.e., noninvertibility) comparable to cryptographic ciphers or hashes. The resultant BioHashes are hence cancellable, i.e., straightforwardly revoked and reissued (via refreshed password or reissued token) if compromised. BioHashing furthermore enhances recognition effectiveness, which is explained in this paper as arising from the Random Multispace Quantization (RMQ) of biometric and external random inputs.

[1] R.M. Bolle, J.H. Connel, and N.K. Ratha, “Biometrics Perils and Patches,” Pattern Recognition, vol. 35, no. 12, pp. 2727-2738, 2002.
[2] D. Maltoni, D. Maio, A.K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition, pp. 301-307. Springer, 2003.
[3] G. Davida, Y. Frankel, and B.J. Matt, “On Enabling Secure Applications through Off-Line Biometrics Identification,” Proc. Symp. Privacy and Security, pp. 148-157, 1998.
[4] J. Daugman, “High Confidence Visual Recognition of Persons by a Test of Statistical Independence,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148-1161, Nov. 1993.
[5] A. Juels and M. Wattenberg, “A Fuzzy Commitment Scheme,” Proc. Sixth ACM Conf. Computer and Comm. Security, pp. 28-36, 1999.
[6] A. Juels and M. Sudan, “A Fuzzy Vault Scheme,” Proc. IEEE Int'l Symp. Information Theory, pp. 408-413, 2002.
[7] T.C. Clancy, N. Kiyavashand, and D.J. Lin, “Secure Smartcard-Based Fingerprint Authentication,” Proc. ACM SIGMM 2993 Multimedia, Biometrics Methods, and Applications Workshop, pp. 45-52, 2003.
[8] Y.W. Chung, D. Moon, S.J. Lee, S.H. Jung, T.H. Kim, and D.S. Ahn, “Automatic Alignment of Fingerprint Features for Fuzzy Fingerprint Vault,” Proc. First SKLOIS Conf. Information Security and Cryptology (CISC 2005), pp. 358-369, 2005.
[9] F. Monrose, M.K. Reiter, and S. Wetzel, “Password Hardening Based on Keystroke Dynamics,” Proc. Sixth ACM Conf. Computer and Comm. Security, pp. 73-82, 1999.
[10] F. Monrose, M.K. Reiter, Q. Li, and S. Wetzel, “Cryptographic Key Generation from Voice,” Proc. IEEE Symp. Security and Privacy, pp.202-213, 2001.
[11] C. Soutar, D. Roberge, A.R. Stoianov, G. Gilroy, and V. Kumar, “Biometrics Encryption,” ICSA Guide to Cryptography, pp. 649-675, 1999.
[12] S. Tulyakov, V.S. Chavan, and V. Govindaraju, “Symmetric Hash Functions for Fingerprint Minutiae,” Proc. Int'l Workshop Pattern Recognition for Crime Prevention, Security, and Surveillance, pp. 30-38, 2005.
[13] R. Ang, S.N. Rei, and L. McAven, “Cancelable Key-Based Fingerprint Templates,” Proc. 10th Australasian Conf. Information Security and Privacy (ACISP '05), pp. 242-252, July 2005.
[14] M. Savvides, B.V.K.V. Kumar, and P.K. Khosla, “Cancellable Biometrics Filters for Face Recognition,” Proc. Int'l Conf. Pattern Recognition, vol. 3, pp. 922-925, 2005.
[15] A. Goh and C.L.D. Ngo, “Computation of Cryptographic Keys from Face Biometrics,” Lecture Notes in Computer Science, vol. 2828, pp. 1-13, 2003.
[16] B.J.A. Teoh and C.L.D. Ngo, “Cancellable Biometrics Featuring with Tokenised Random Number,” Pattern Recognition Letters, vol. 26, no. 10, pp. 1454-1460, 2005.
[17] B.J.A. Teoh, C.L.D. Ngo, and A. Goh, “Personalised Cryptographic Key Generation Based on FaceHashing,” Computers and Security J., vol. 23, no. 7, pp. 606-614, 2004.
[18] M. Turk and A. Pentland, “Eigenfaces for Recognition,” J.Cognitive NeuroScience, vol. 3, no. 1, pp. 71-86, 1991.
[19] P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman, “Eigenfaces versus Fisherfaces: Recognition Using Class Specific Linear Projection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.
[20] W.K. Yip, A. Goh, B.J.A. Teoh, and C.L.D. Ngo, “Cryptographic Keys from Dynamic Handsignatures with Biometric Secrecy Preservation and Replaceability,” Proc. Fourth IEEE Workshop Automatic Identification Advanced Technologies (AutoID '05), pp.27-32, Oct. 2005.
[21] J.W. Demmel and N.J. Higham, “Improved Error Bounds for Underdetermineded System Solvers,” Technical Report CS-90-113, Computer Science Dept., Univ. of Tennessee, K noxville, Aug. 1990.
[22] J. Daugman, “Biometrics Decision Landscapes,” Technical Report no. 482, Computer Laboratory, Cambridge Univ., 2002.
[23] A. Menezes, P.V. Oorschot, and S. Vanstone, Handbook of Applied Cryptography. CRC Press, 1996.
[24] W.B. Johnson and J. Lindenstrauss, “Extension of Lipschitz Mapping into a Hilbert Space,” Proc. Conf. Modern Analysis and Probability, pp. 189-206, 1984.
[25] R.I. Arriaga and S. Vempala, “An Algorithmic Theory of Learning: Robust Concepts and Random Projection,” Proc. 40th Ann. Symp. Foundations of Computer Science, p. 616, Oct. 1999.
[26] W. Hoffmann, “Iterative Algorithms for Gram-Schmidt Orthogonalization,” Computing, vol. 41, no. 4, pp. 335-348, 1989.
[27] S. Kaski, “Dimensionality Reduction by Random Mapping,” Proc. Int'l Joint Conf. Neural Networks, vol. 1, pp. 413-418, 1998.
[28] F.N. David, “The Moments of the z and F Distributions,” Biometrika, vol. 36, pp. 394-403, 1949.
[29] J. Daugman, “The Importance of Being Random: Statistical Principles of Iris Recognition,” Pattern Recognition, vol. 36, no. 2, pp. 279-291, 2003.
[30] R. Viveros, K. Balasubramanian, and N. Balakrishnan, “Binomial and Negative Binomial Analogues under Correlated Bernoulli Trials,” Am. Statististics, vol. 48, no. 3, pp. 243-247, 1984.
[31] P. Phillips, H. Moon, P. Rauss, and S. Rizvi, “The FERET Database and Evaluation Methodology for Face Recognition Algorithms,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 137-143, 1997.
[32] C.L.D. Ngo, A. Goh, and B.J.A. Teoh, “Front-View Facial Feature Extraction Using Dynamic Symmetry,” technical report, Multimedia Univ., 2004.

Index Terms:
Cancellable biometrics, BioHashing, random multispace quantization, face recognition.
Citation:
Andrew B.J. Teoh, Alwyn Goh, David C.L. Ngo, "Random Multispace Quantization as an Analytic Mechanism for BioHashing of Biometric and Random Identity Inputs," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 1892-1901, Dec. 2006, doi:10.1109/TPAMI.2006.250
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