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http://dx.doi.org/10.9708/jksci.2019.24.02.035

A Study on Reconstruction Vulnerability of Daugman's Iriscode  

Youn, Soung-Jo (Dept. of Convergence Software, Soongsil University)
Anusha, B.V.S (Dept. of Electrical engineering, IIT Bombay)
Kim, Gye-Young (Dept. of Software, Soongsil University)
Publication Information
Journal of the Korea Society of Computer and Information / v.24, no.2, 2019 , pp. 35-40 More about this Journal
Abstract
In this paper, we propose a technique to reconstruct the iris image from the iris code by analyzing the process of generating the iris code and calculating it inversely. Iris recognition is an authentication method for authenticating an individual's identity by using iris information of an eye having unique information of an individual. The iris recognition extracts the features of the iris from the iris image, creates the iris code, and determines whether to authenticate using the corresponding code. The iris recognition method using the iris code is a method proposed by Daugman for the first time and is widely used as a representative method of iris recognition technology currently used commercially. In this paper, we restore the iris image with only the iris code, and test whether the reconstructed image and the original image can be recognized, and analyze restoration vulnerability of Daugman's iris code.
Keywords
John Daugman; Iris recognition; Iris code; gabor filter; Image reconstruction;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 J. Wayman, A. Jain, D. Maltoni, D. Maio, "Biometric Systems", Technology, Design and Performance Evaluation, 2005.
2 J. Daugman, "How Iris Recognition Works", IEEE Trans. on Circuits and Systems for Video Technology, Vol. 14, No. 1, pp. 21-30, Jan. 2004.   DOI
3 Philip Bontrager, Aditi Roy, Julian Togelius, Nasir Memon, Arun Ross, "DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution", Computer Vision and Pattern Recognition, May 2017.
4 J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. arde-Farley, S. Ozair, A. C. Courville, Y. Bengio, "Generative adversarial nets", In Proceedings of NIPS, pp. 2672-2680, 2014.
5 J. Galbally, A. Ross, M. Gomez-Barrero, "Iris image reconstruction from binary templates: An efficient probabilistic approach based on genetic algorithms", Comput. Vis. Image Understanding, vol. 117, no. 10, pp. 1512-1525, Oct. 2013.   DOI
6 J. Daugman, "High Confidence Visual Recognition of Persons by a Test of Statistical Independence", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148-1161, Nov. 1993.   DOI
7 Man-Ki Kim, Samuel Lee, Gye-Young Kim, Fake Iris Image Detection based on Watermark", Journal of The Korea Society of Computer and Information, vol. 23, No.4, pp. 33-39, Apr. 2018.   DOI
8 Jaeyeong Jang, Hoeyul Gim, "Trend of iris recognition technology", The Institute of electronics engineers of korea, pp. 17-23, Nov. 1999.
9 Chang-Soo Choi, Jeong-Man Seo, Byoung-Min Jun, "Rotation-Invariant Iris Recognition Method Based on Zernike Moments", Journal of The Korea Society of Computer and Information, vol. 17, No. 2, Feb. 2012.
10 Prateek Verma, Maheedhar Dubey, Somak Basu, Praveen Verma, "Hough Transform Method for Iris Recognition-A Biometric Approach", International Journal Of Engineering and Innovative Technology, Volume 1, Issue 6, pp. 43-48, Jun. 2012.
11 WoongBae Yoon, TaeYun Kim, JiEun Oh, KwangGi Kim, "A Novel Circle Detection Algorithm for Iris Segmentatio", Journal of Korea Multimedia Society, Vol. 16, No. 12, pp. 1385-1392, Dec. 2013.   DOI