Robust and Secure Iris Recognition

Iris biometric entails using the patterns on the iris as a biometric for personal authentication. It has additional benefits over contact-based biometrics such as fingerprints and hand geometry. However, iris biometric often suffers from the following three challenges: ability to handle unconstrained acquisition, privacy enhancement without compromising security, and robust matching. This chapter discusses a unified framework based on sparse representations and random projections that can address these issues simultaneously. Furthermore, recognition from iris videos as well as generation of cancelable iris templates for enhancing the privacy and security is also discussed.

[1]  Rama Chellappa,et al.  Secure and Robust Iris Recognition Using Random Projections and Sparse Representations , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  David L. Donoho,et al.  High-Dimensional Centrally Symmetric Polytopes with Neighborliness Proportional to Dimension , 2006, Discret. Comput. Geom..

[3]  Xingzhao Liu,et al.  Nonlinear Frequency Scaling Algorithm for High Squint Spotlight SAR Data Processing , 2008, EURASIP J. Adv. Signal Process..

[4]  Andrew Beng Jin Teoh,et al.  Random Multispace Quantization as an Analytic Mechanism for BioHashing of Biometric and Random Identity Inputs , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[7]  Nalini K. Ratha,et al.  Biometric perils and patches , 2002, Pattern Recognit..

[8]  Bruce A. Draper,et al.  Overview of the Multiple Biometrics Grand Challenge , 2009, ICB.

[9]  Yingzi Du,et al.  Multi-level iris video image thresholding , 2009, 2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications.

[10]  Pierre Vandergheynst,et al.  Compressed Sensing and Redundant Dictionaries , 2007, IEEE Transactions on Information Theory.

[11]  Bernadette Dorizzi,et al.  Cancelable iris biometrics and using Error Correcting Codes to reduce variability in biometric data , 2009, CVPR.

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

[13]  Rama Chellappa,et al.  Sectored Random Projections for Cancelable Iris Biometrics , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[14]  Bernadette Dorizzi,et al.  Specific Texture Analysis for Iris Recognition , 2005, AVBPA.

[15]  Patrick J. Flynn,et al.  Image Averaging for Improved Iris Recognition , 2009, ICB.

[16]  Natalia A. Schmid,et al.  Performance analysis of iris-based identification system at the matching score level , 2006, IEEE Trans. Inf. Forensics Secur..

[17]  Rama Chellappa,et al.  Sparsity inspired selection and recognition of iris images , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

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

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

[20]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

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

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

[23]  Dexin Zhang,et al.  Efficient iris recognition by characterizing key local variations , 2004, IEEE Transactions on Image Processing.

[24]  Aswin C. Sankaranarayanan,et al.  Compressive Sensing , 2008, Computer Vision, A Reference Guide.

[25]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[26]  Prabir Bhattacharya,et al.  Iris Recognition with Support Vector Machines , 2006, ICB.

[27]  Yair Frankel,et al.  On enabling secure applications through off-line biometric identification , 1998, Proceedings. 1998 IEEE Symposium on Security and Privacy (Cat. No.98CB36186).

[28]  Natalia A. Schmid,et al.  A method for selecting and ranking quality metrics for optimization of biometric recognition systems , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[29]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[31]  P. Jonathon Phillips,et al.  Meta-Analysis of Third-Party Evaluations of Iris Recognition , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

[32]  Mei Xie,et al.  Iris Recognition Based on DLDA , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[33]  Nalini K. Ratha,et al.  Cancelable iris biometric , 2008, 2008 19th International Conference on Pattern Recognition.

[34]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[35]  Yingzi Du,et al.  Using 2D Log-Gabor spatial filters for iris recognition , 2006, SPIE Defense + Commercial Sensing.