Predicting iris vulnerability to direct attacks based on quality related features

A new vulnerability prediction scheme for direct attacks to iris recognition systems is presented. The objective of the novel technique, based on a 22 quality related parameterization, is to discriminate beforehand between real samples which are easy to spoof and those more resistant to this type of threat. The system is tested on a database comprising over 1,600 real and fake iris images proving to have a high discriminative power reaching an overall rate of 84% correctly classified real samples for the dataset considered. Furthermore, the detection method presented has the added advantage of needing just one iris image (the same used for verification) to decide its degree of robustness against spoofing attacks.

[1]  Stephanie Schuckers,et al.  Iris quality assessment and bi-orthogonal wavelet based encoding for recognition , 2009, Pattern Recognit..

[2]  Natalia A. Schmid,et al.  Global and local quality measures for NIR iris video , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[3]  Nalini K. Ratha,et al.  Enhancing security and privacy in biometrics-based authentication systems , 2001, IBM Syst. J..

[4]  Julian Fiérrez,et al.  An evaluation of indirect attacks and countermeasures in fingerprint verification systems , 2011, Pattern Recognit. Lett..

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

[6]  Erik Reinhard,et al.  An Ocularist's Approach to Human Iris Synthesis , 2003, IEEE Computer Graphics and Applications.

[7]  Tieniu Tan,et al.  Robust and Fast Assessment of Iris Image Quality , 2006, ICB.

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

[9]  Nalini K. Ratha,et al.  An Analysis of Minutiae Matching Strength , 2001, AVBPA.

[10]  Albert Ali Salah,et al.  Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms , 2009, IEEE Transactions on Information Forensics and Security.

[11]  Julian Fiérrez,et al.  Biosec baseline corpus: A multimodal biometric database , 2007, Pattern Recognit..

[12]  J. L. Wayman,et al.  Best practices in testing and reporting performance of biometric devices. , 2002 .

[13]  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 .

[14]  Julian Fiérrez,et al.  Iris liveness detection based on quality related features , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).

[15]  Anil K. Jain,et al.  Biometric Template Security , 2008, EURASIP J. Adv. Signal Process..

[16]  Julian Fiérrez,et al.  Evaluation of direct attacks to fingerprint verification systems , 2011, Telecommun. Syst..

[17]  Ton van der Putte,et al.  Biometrical Fingerprint Recognition: Don't Get Your Fingers Burned , 2001, CARDIS.

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

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

[20]  Julian Fiérrez,et al.  Bayesian Hill-Climbing Attack and Its Application to Signature Verification , 2007, ICB.

[21]  Dexin Zhang,et al.  Personal Identification Based on Iris Texture Analysis , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Julian Fiérrez,et al.  A Comparative Study of Fingerprint Image-Quality Estimation Methods , 2007, IEEE Transactions on Information Forensics and Security.

[23]  Natalia A. Schmid,et al.  Image quality assessment for iris biometric , 2006, SPIE Defense + Commercial Sensing.

[24]  Anil K. Jain,et al.  Localized Iris Image Quality Using 2-D Wavelets , 2006, ICB.

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

[26]  Yingzi Du,et al.  Feature correlation evaluation approach for iris feature quality measure , 2010, Signal Process..

[27]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[28]  Alessandra Lumini,et al.  An evaluation of direct attacks using fake fingers generated from ISO templates , 2010, Pattern Recognit. Lett..

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