Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
권호 표지
DOI QR코드

DOI QR Code

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)
  • Received : 2019.01.03
  • Accepted : 2019.02.03
  • Published : 2019.02.28
Download PDF
⟨ Previous Next ⟩

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

CPTSCQ_2019_v24n2_35_f0001.png 이미지

Fig. 1. Example of iris reconstruction using genetic algorithm

CPTSCQ_2019_v24n2_35_f0002.png 이미지

Fig. 2. Iris localization and segmentation

CPTSCQ_2019_v24n2_35_f0003.png 이미지

Fig. 3. Example of Iris normalization

CPTSCQ_2019_v24n2_35_f0004.png 이미지

Fig. 5. Example of image reconstruction process

CPTSCQ_2019_v24n2_35_f0005.png 이미지

Fig. 6. Example of Calculate Restore Values

CPTSCQ_2019_v24n2_35_f0006.png 이미지

Fig. 7. Restore Process Pseudocode

CPTSCQ_2019_v24n2_35_f0007.png 이미지

Fig. 9. Hamming distance graph comparing the iris of another person

CPTSCQ_2019_v24n2_35_f0008.png 이미지

Fig. 10. Hamming distance graph comparing the iris of the same person

CPTSCQ_2019_v24n2_35_f0009.png 이미지

Fig. 11. Hamming distance graph comparing original image and restored image

CPTSCQ_2019_v24n2_35_f0010.png 이미지

Fig. 4. (a)normalized iris image, (b)real part feature, (c) imaginary part feature, (d) iris code

CPTSCQ_2019_v24n2_35_f0011.png 이미지

Fig. 8. (a)Original image, (b)Real part and imaginary part code, (c)Restored iris image, (d)Coordinate system converted iris image

Table 1. Hamming distance threshold based on FAR

CPTSCQ_2019_v24n2_35_t0001.png 이미지

Table 2. Restoration success rate based on FAR

CPTSCQ_2019_v24n2_35_t0002.png 이미지

References

  1. J. Wayman, A. Jain, D. Maltoni, D. Maio, "Biometric Systems", Technology, Design and Performance Evaluation, 2005.
  2. 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.
  3. 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.
  4. 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. https://doi.org/10.1016/j.cviu.2013.06.003
  5. 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. https://doi.org/10.9708/JKSCI.2018.23.04.033
  6. Jaeyeong Jang, Hoeyul Gim, "Trend of iris recognition technology", The Institute of electronics engineers of korea, pp. 17-23, Nov. 1999.
  7. 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.
  8. 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. https://doi.org/10.1109/34.244676
  9. 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.
  10. 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. https://doi.org/10.9717/kmms.2013.16.12.1385
  11. J. Daugman, "How Iris Recognition Works", IEEE Trans. on Circuits and Systems for Video Technology, Vol. 14, No. 1, pp. 21-30, Jan. 2004. https://doi.org/10.1109/TCSVT.2003.818350