From the Iriscode to the Iris: A New Vulnerability of Iris Recognition Systems

A binary iriscode is a very compact representation of an iris image, and, for a long time, it has been assumed that it did not contain enough information to allow the reconstruction of the original iris. The present work proposes a novel probabilistic approach to reconstruct iris images from binary templates and analyzes to what extentthe reconstructed samples are similar tothe original ones (that is, those from which the templates were extracted). The performance of the reconstruction technique is assessed by estimating the success chances of an attack carried out with the synthetic iris patterns against a commercial iris recognition system. The experimental results show that the reconstructed images are very realistic and that, even though a human expert would not be easily deceived by them, there is a high chance that they can break into an iris recognition system. Furthermore, as the proposed reconstruction methodology is able to generate not just one, but a large amount of iris-like patterns with iriscodes which fall within the intra-class variability of a genuine user, the proposed method has other potential applications including enrollment improvement or individuality studies.

[1]  Sébastien Marcel,et al.  On the vulnerability of face verification systems to hill-climbing attacks , 2010, Pattern Recognit..

[2]  Anil K. Jain,et al.  Biometric cryptosystems: issues and challenges , 2004, Proceedings of the IEEE.

[3]  Zohreh Mousavinasab,et al.  Biometric Systems , 2013 .

[4]  James E. Baker,et al.  Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.

[5]  George W. Quinn,et al.  IREX I: Performance of Iris Recognition Algorithms on Standard Images | NIST , 2009 .

[6]  K.W. Bowyer,et al.  The Best Bits in an Iris Code , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[8]  Pietro Perona,et al.  Visual Identification by Signature Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Luminita Vasiu,et al.  Biometric Recognition - Security and Privacy Concerns , 2004, ICETE.

[10]  Tieniu Tan,et al.  Counterfeit iris detection based on texture analysis , 2008, 2008 19th International Conference on Pattern Recognition.

[11]  Dexin Zhang,et al.  DCT-Based Iris Recognition , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Julian Fiérrez,et al.  Improving the Enrollment in Dynamic Signature Verfication with Synthetic Samples , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[13]  Venu Govindaraju,et al.  Advances in Biometrics: Sensors, Algorithms and Systems , 2007 .

[14]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[15]  Marios Savvides,et al.  How to Generate Spoofed Irises From an Iris Code Template , 2011, IEEE Transactions on Information Forensics and Security.

[16]  Patrick J. Flynn,et al.  Image understanding for iris biometrics: A survey , 2008, Comput. Vis. Image Underst..

[17]  Julian Fiérrez,et al.  Direct Attacks Using Fake Images in Iris Verification , 2008, BIOID.

[18]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[19]  John Daugman,et al.  How iris recognition works , 2002, IEEE Transactions on Circuits and Systems for Video Technology.

[20]  Arun Ross,et al.  Generating Synthetic Irises by Feature Agglomeration , 2006, 2006 International Conference on Image Processing.

[21]  John Daugman,et al.  Probing the Uniqueness and Randomness of IrisCodes: Results From 200 Billion Iris Pair Comparisons , 2006, Proceedings of the IEEE.

[22]  Libor Masek,et al.  MATLAB Source Code for a Biometric Identification System Based on Iris Patterns , 2003 .

[23]  John Daugman,et al.  New Methods in Iris Recognition , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[24]  Alejandro F. Frangi,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. , 2022 .

[25]  Sharath Pankanti,et al.  On the individuality fingerprints , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[26]  Sharath Pankanti,et al.  Biometrics: a tool for information security , 2006, IEEE Transactions on Information Forensics and Security.

[27]  Sharath Pankanti,et al.  Iris individuality: a partial iris model , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[28]  Bernadette Dorizzi,et al.  Guide to Biometric Reference Systems and Performance Evaluation , 2009 .

[29]  Bruce Schneier,et al.  Inside risks: the uses and abuses of biometrics , 1999, CACM.

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

[31]  Ramaswamy Chandramouli,et al.  NIST Special Publication 800-76-1 Biometric Data Specification for Personal Identity Verification , 2007 .

[32]  A. Anthony Irudhayaraj,et al.  Biometric system , 2011, 2011 3rd International Conference on Electronics Computer Technology.

[33]  Alessandra Lumini,et al.  Fingerprint Image Reconstruction from Standard Templates , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Christer Bergman.  Match-on-Card for Secure and Scalable Biometric Authentication , 2008 .