Feature Set Fusion for Spoof Iris Detection
Iris recognition is considered as one of the most promising noninvasive biometric systems providing automated human identification. Numerous programs, like unique ID program in India - Aadhar, include iris biometric to provide distinctive identity identification to citizens. The active area is usually captured under non ideal imaging conditions. It usually suffers from poor brightness, low contrast, blur due to camera or subject's relative movement and eyelid eyelash occlusions. Besides the technical challenges, iris recognition started facing sophisticated threats like spoof attacks. Therefore it is vital that the integrity of such large scale iris deployments must be preserved. This paper presents the development of a new spoof resistant approach which exploits the statistical dependencies of both general eye and localized iris regions in textural domain using spatial gray level dependence matrix (SGLDM), gray level run length matrix (GLRLM) and contourlets in transform domain. We did experiments on publicly available fake and lens iris image databases. Correct classification rate obtained with ATVS-FIr iris database is 100% while it is 95.63% and 88.83% with IITD spoof iris databases respectively.
Keywords:iris recognition, liveness detection, spoofing, print attacks, contact lens attacks
K. W. Bowyer, K. Hollingsworth, P. J. Flynn, “Image understanding for iris biometrics: a survey”, Computer Vision and Image Understanding, Vol. 110, No. 2, pp. 281-307, 2008 DOI: https://doi.org/10.1016/j.cviu.2007.08.005
Unique Identification Authority of India, Role of biometric technology in aadhar enrollment, available at https://pdfs.semanticscholar.org
J. Lee, “Spoofing iris recognition technology with pictures”, available at: https://www.biometricupdate.com/201503/spoofing-iris-recognition-technology-with-pictures, 2015
J. S. Doyle, P. J. Flynn, K. W. Bowyer, “Automated classification of contact lens type in iris images”, 2013 International Conference on Biometrics (ICB), Madrid, Spain, June 7-13, 2013 DOI: https://doi.org/10.1109/ICB.2013.6612954
N. Kohli, D. Yadav, M. Vatsa, R. Singh, “Revisiting iris recognition with color cosmetic contact lenses”, 2013 International Conference on Biometrics (ICB), Madrid, Spain, June 7-13, 2013 DOI: https://doi.org/10.1109/ICB.2013.6613021
J. Daugman, “Demodulation by complex-valued wavelets for stochastic pattern recognition”, International Journal of Wavelets, Multiresolution and Information Processing, Vol. 1, No. 1, pp. 1–17, 2003 DOI: https://doi.org/10.1142/S0219691303000025
D. Yadav, N. Kohli, J. S. Doyle, R. Singh, M. Vatsa, K. W. Bowyer, “Unravelling the effect of textured contact lenses on iris recognition”, IEEE Transactions on Information Forensics and Security, Vol. 9, No. 5, pp. 851-862, 2014 DOI: https://doi.org/10.1109/TIFS.2014.2313025
Biometric Recognition Group–ATVS, ATVS-Fir, Fake Iris Images Database, available at: https://atvs.ii.uam.es/atvs/fir_db.html
Image Analysis and Biometrics Lab at in IIIT Delhi, IIITD Contact Lens Database
J. Gallaby, J. Ortiz-Lopez, J. Fierrez, J. Ortega-Garcia, “Iris liveness detection based on quality related features”, 5th IAPR International Conference on Biometrics (ICB), New Delhi, India, pp. 271-276, March 29 - April 1, 2012
C.-W. Tan, A. Kumar, “Integrating occular and iris descriptors for fake iris recognition”, 2014 International Workshop on Biometrics and Forensics (IWBF), Valletta, Malta, March 27-28, 2014 DOI: https://doi.org/10.1109/IWBF.2014.6914251
X. He, S. An, P. Shi, “Statistical texture analysis-based approach for fake iris detection using support vector machines,” Lecture Notes in Computer Science, Vol. 4642, pp. 540-546, 2007 DOI: https://doi.org/10.1007/978-3-540-74549-5_57
Z. Wei, X. Qiu, Z. Sun, T. Tan, “Counterfeit iris detection based on texture analysis,” 19th International Conference on Pattern Recognition, Tampa, USA, December 8-11, 2008
Z. He, Z. Sun, T. Tan, Z. Wei, “Efficient iris spoof detection via boosted local binary patterns”, Lecture Notes in Computer Science, Vol. 5558, pp. 1080-1090, 2009 DOI: https://doi.org/10.1007/978-3-642-01793-3_109
H. Zhang, Z. Sun, T. Tan, “Contact lens detection based on weighted LBP”, 20th International Conference on Pattern Recognition, Istanbul, Turkey, pp. 4279-4282, August 23-26, 2010.
W. S. -A. Fathy, H. S. Ali, “Entropy with local binary patterns for efficient iris liveness detection”, in: Wireless Personal Communications, Springer Science+Business Media, LLC, 2017 DOI: https://doi.org/10.1007/s11277-017-5089-z
J. Daugman, “How iris recognition works”, IEEE Transactions on Circuits and Systems for VideoTechnology, Vol. 14, No. 1, pp. 21-30, 2004 DOI: https://doi.org/10.1109/TCSVT.2003.818350
R. M. Haralick, K. Shanmugam, I. Dinstein, “Textural features for image classification”, IEEE Transactions on Systems, Man and Cybernetics, Vol. SMC-3, No.6, pp. 610-62, 1973 DOI: https://doi.org/10.1109/TSMC.1973.4309314
D. Gragnaniello, G. Poggi, C. Sansone, L. Verdoliva, “Contact lens detection and classification in iris images through scale invariant descriptor”, 10th International Conference on Signal-Image Technology and Internet-Based Systems, Marrakech, Morocco, November 23-27, 2014 DOI: https://doi.org/10.1109/SITIS.2014.35
Support Vector Machines Toolbox, available at https://sourceforge.net/
M. N. Do, M. Vetterli, “The Contourlet transform: an efficient directional multiresolution image representation”, IEEE Transactions on Image Processing, Vol. 14, No. 12, pp. 2091–2106, 2005 DOI: https://doi.org/10.1109/TIP.2005.859376
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