Optimal Iris Fuzzy Sketches

Fuzzy sketches, introduced as a link between biometry and cryptography, are a way of handling biometric data matching as an error correction issue. We focus here on iris biometrics and look for the best error-correcting code in that respect. We show that two-dimensional iterative min-sum decoding leads to results near the theoretical limits. In particular, we experiment our techniques on the iris challenge evaluation (ICE) database and validate our findings.

[1]  Ashok A. Ghatol,et al.  Iris recognition: an emerging biometric technology , 2007 .

[2]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[3]  Rafail Ostrovsky,et al.  Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data , 2004, SIAM J. Comput..

[4]  Niclas Wiberg,et al.  Codes and Decoding on General Graphs , 1996 .

[5]  John Daugman,et al.  The importance of being random: statistical principles of iris recognition , 2003, Pattern Recognit..

[6]  R. P. Wildes Iris recognition : An emerging biometric technology : Automated biometrics , 1997 .

[7]  Gérard D. Cohen,et al.  The wiretap channel applied to biometrics , 2004 .

[8]  Richard P. Wildes,et al.  Iris recognition: an emerging biometric technology , 1997, Proc. IEEE.

[9]  Qiang Tang,et al.  An Application of the Goldwasser-Micali Cryptosystem to Biometric Authentication , 2007, ACISP.

[10]  G. Zemor,et al.  Syndrome-coding for the wiretap channel revisited , 2006, 2006 IEEE Information Theory Workshop - ITW '06 Chengdu.

[11]  F. MacWilliams,et al.  The Theory of Error-Correcting Codes , 1977 .

[12]  John Daugman The importance ofbeing random: statistical principles ofiris recognition , 2003 .

[13]  Pim Tuyls,et al.  Capacity and Examples of Template-Protecting Biometric Authentication Systems , 2004, ECCV Workshop BioAW.

[14]  Anton H. M. Akkermans,et al.  Face recognition with renewable and privacy preserving binary templates , 2005, Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05).

[15]  Michael Boyd,et al.  Iris Recognition , 2006 .

[16]  John Daugman,et al.  High Confidence Visual Recognition of Persons by a Test of Statistical Independence , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Feng Hao,et al.  Combining Crypto with Biometrics Effectively , 2006, IEEE Transactions on Computers.

[18]  Irving S. Reed,et al.  A class of multiple-error-correcting codes and the decoding scheme , 1954, Trans. IRE Prof. Group Inf. Theory.

[19]  David E. Muller,et al.  Application of Boolean algebra to switching circuit design and to error detection , 1954, Trans. I R E Prof. Group Electron. Comput..

[20]  Yair Frankel,et al.  Perfectly Secure Authorization and Passive Identification for an Error Tolerant Biometric System , 1999, IMACC.

[21]  Rafail Ostrovsky,et al.  Secure Remote Authentication Using Biometric Data , 2005, EUROCRYPT.

[22]  Gilles Zémor,et al.  Generalized coset schemes for the wire-tap channel: application to biometrics , 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..

[23]  Robert Michael Tanner,et al.  A recursive approach to low complexity codes , 1981, IEEE Trans. Inf. Theory.

[24]  Raymond N. J. Veldhuis,et al.  Practical Biometric Authentication with Template Protection , 2005, AVBPA.

[25]  Martin Wattenberg,et al.  A fuzzy commitment scheme , 1999, CCS '99.

[26]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .