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Real-time iris segmentation and its implementation on FPGA

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Abstract

This paper presents a real-time iris segmentation technique that is well suited to a fast implementation on an FPGA. One major hurdle associated with iris segmentation techniques is the use of iterative processes that lead to expensive hardware implementations. To circumvent this, the proposed algorithm uses the sign image obtained from subtracting the background, along with morphological operators to localise the pupil. The outer boundary is located by first normalising a selected image region that contains the iris, and then using a first-order gradient operator. The proposed non-iterative algorithm is implemented on an FPGA. Four near infrared (NIR) iris public databases, namely: CASIA-IrisV3-Lamp, MMU v1.0, ND-IRIS-0405 and NIST ICE 2005, are used to test the proposed algorithm. The proposed method for iris segmentation and normalization gives much better accuracy than the existing state-of-the-art methods implemented on hardware. The proposed realisation requires about 45% fewer logic registers and 52% fewer logic elements than the existing state-of-the-art implementations.

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References

  1. Khan, T.M., Khan, M.A., Malik, S.A., Khan, S.A., Bashir, T., Dar, A.H.: Automatic localization of pupil using eccentricity and iris using gradient based method. Opt. Lasers Eng. 49(2), 177–187 (2011)

    Article  Google Scholar 

  2. Ibrahim, M.T., Khan, T.M., Khan, S.A., Khan, M.A., Guan, L.: Iris localization using local histogram and other image statistics. Opt. Lasers Eng. 50(5), 645–654 (2012)

    Article  Google Scholar 

  3. Cui, J., Wang, Y., Tan, T., Ma, L., Sun, Z.: A fast and robust iris localization method based on texture segmentation. Biom. Technol. Hum. Identif. 5404, 401–408 (2004)

    Article  Google Scholar 

  4. Dey, S., Samanta, D.: A novel approach to iris localization for iris biometric processing. Int. J. Biol. Biomed. Med. Sci. 1(5), 293–304 (2007)

    Google Scholar 

  5. Lopez, M., Daugman, J., Canto, E.: Hardware-software co-design of an iris recognition algorithm. IET Inf. Secur. 5(1), 60–68 (2011)

    Article  Google Scholar 

  6. Kumar, V., Asati, A., Gupta, A.: Hardware implementation of a novel edge-map generation technique for pupil detection in NIR images. Eng. Sci. Technol. Int. J. 20(2), 694–704 (2017)

    Google Scholar 

  7. Daugman, J.: How iris recognition works. In: International Conference on Image Processing, vol. 1, pp. I-33–I-36 (2002)

  8. Chen, Y., Adjouadi, M., Han, C., Wang, J., Barreto, A., Rishe, N., Andrian, J.: A highly accurate and computationally efficient approach for unconstrained iris segmentation. Image Vis. Comput. 28(2), 261–269 (2010)

    Article  Google Scholar 

  9. Daugman, J.: Biometric personal identification system based on iris analysis. USA A Patent US5 291 560 A (1994)

  10. Ngo, H.T., Rakvic, R.N., Broussard, R.P., Ives, R.W.: Resource-aware architecture design and implementation of Hough transform for a real-time iris boundary detection system. IEEE Trans. Consum. Electron. 60(3), 485–492 (2014)

    Article  Google Scholar 

  11. Grabowski, K., Napieralski, A.: Hardware architecture optimized for iris recognition. IEEE Trans. Circuits Syst. Video Technol. 21(9), 1293–1303 (2011)

    Article  Google Scholar 

  12. Ross, A., Shah, S.: Segmenting non-ideal irises using geodesic active contours. In: Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference, Sept 2006, pp. 1–6 (2006)

  13. Ibrahim, M.T., Mehmood, T., Khan, M.A., Guan, L.: A novel and efficient feedback method for pupil and iris localization. In: International Conference on Image Analysis and Recognition, LNCS, vol. 6754, pp. 79–88 (2011)

  14. Ibrahim, M.T., Khan, T.M., Khan, M.A., Ling, G.: Automatic segmentation of pupil using local histogram and standard deviation. In: Visual Communications and Image Processing, vol. 7744. SPIE, pp. 77 442S–77 442S (2010)

  15. Wildes, R.: Iris recognition: an emerging biometric technology. Proc. IEEE 85(9), 1348–1363 (1997)

    Article  Google Scholar 

  16. Khan, T.M., Kong, Y., Khan, M.A.: Hardware implementation of fast pupil segmentation using region properties. In: The International Conference on Quality Control by Artificial Vision, vol. 9534. SPIE, 95 340F–95 340F (2015)

  17. Ngo, H., Shafer, J., Ives, R., Rakvic, R., Broussard, R.: Real time iris segmentation on FPGA. In: IEEE 23rd International Conference on Application-Specific Systems, Architectures and Processors (ASAP), pp. 1–7, July (2012)

  18. Kumar, V., Asati, A., Gupta, A.: Hardware implementation of a novel edge-map generation technique for pupil detection in NIR images. Eng. Sci. Technol. Int. J. 20(2), 694–704 (2017)

    Google Scholar 

  19. Kumar, V., Asati, A., Gupta, A.: Hardware accelerators for iris localization. J. Signal Process. Syst. 90(4), 655–671 (2018)

    Article  Google Scholar 

  20. Avey, J.: An FPGA-based hardware accelerator for iris segmentation. Master’s thesis, Iowa State University (2018)

  21. Al-Mamory, H.: Iris detection using morphology. J. Babylon Univ. Pure Appl. Sci. 22(9), 2277–2282 (2014)

    Google Scholar 

  22. Chen, Y., Liu, Y., Zhu, X.: Robust iris segmentation algorithm based on self-adaptive chan Vese level set model. J. Electron. Imaging 24(4), 043 012 1–043 012 12 (2015)

    Article  Google Scholar 

  23. Zhang, S., Hou, G., Sun, Z.: Eyelash removal using light field camera for iris recognition. In: Chinese Conference on Biometric Recognition, vol. 8833. LNCS, pp. 319–327 (2014)

  24. Ali, M.A.M., Tahir, N.M.: Half iris Gabor based iris recognition. In: IEEE 10th International Colloquium on Signal Processing & Its Applications (CSPA), pp. 282 –287 (2014)

  25. Bailey, D.G.: Design for Embedded Image Processing on FPGAs. Wiley, Hoboken (2011)

    Book  Google Scholar 

  26. Bailey, D.G.: Efficient implementation of greyscale morphological filters. In: International Conference on Field Programmable Technology, pp. 421–42 (2010)

  27. Rosenfeld, A., Pfaltz, J.: Sequential operations in digital picture processing. J. ACM 13(4), 471–494 (1996)

    Article  Google Scholar 

  28. Klaiber, M.J., Bailey, D.G., Baroud, Y.O., Simon, S.: A resource-efficient hardware architecture for connected components analysis. IEEE Trans. Circuits Syst. Video Technol. 26(7), 1334–1349 (2016)

    Article  Google Scholar 

  29. MMU iris database (2007). [Online]. http://www.cs.princeton.edu/~andyz/irisrecognition. Accessed Dec 2016

  30. Specifications of CASIA iris image database. Chinese Academy of Sciences. [Online]. http://english.ia.cas.cn/db/201610/t20161026_169399.html. Accessed Dec 2016

  31. Bowyer, K.W., Flynn, P.J.: The ND-IRIS-0405 iris image dataset. Tech. Rep. arXiv:1606.04853

  32. Phillips, P.J., Scruggs, W.T., O’Toole, A.J., Flynn, P.J., Bowyer, K.W., Schott, C.L., Sharpe, M.: FVRT 2006 and ICE 2006 large-scale experimental results. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 831–846 (2010)

    Article  Google Scholar 

  33. Phillips, P.J., Bowyer, K.W., Flyn, P.J., Liu, X., Scruggs, W.T.: The iris challenge evaluation 2005. In: Biometrics: Theory, Applications and Systems (2008)

  34. Masek, L., Kovesi, P.: Matlab source code for a biometric identification system based on iris patterns. The School of Computer Science and Software Engineering, The University of Western Australia, Tech. Rep. (2003)

  35. Kumar, V., Asati, A., Gupta, A.: Memory-efficient architecture of circle Hough transform and its FPGA implementation for iris localisation. IET Image Process. 12(8), 1753–1761 (2018)

    Article  Google Scholar 

  36. Rakvic, R.N., Ulis, B.J., Broussard, R.P., Ives, R.W., Steiner, N.: Parallelizing iris recognition. IEEE Trans. Inf. Forensics Secur. 4(4), 812–823 (2009)

    Article  Google Scholar 

  37. Ng, R.Y.F., Tay, Y.H., Mok, K.M.: Iris verification algorithm based on texture analysis and its implementation on DSP. In: International Conference on Signal Acquisition and Processing (ICSAP2009), pp. 198–202 (2009)

  38. Giacometto, F.J., Vilardy, J.M., Torres, C.O., Mattos, L.: Design and implementation of an algorithm for creating templates for the purpose of iris biometric authentication through the analysis of textures implemented on a FPGA. J. Phys. 274(1), 1–13 (2011)

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan

    Tariq M. Khan

  2. School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand

    Donald G. Bailey

  3. Biometric and Sensor Lab, Effat University, Jeddah, Saudi Arabia

    Mohammad A. U. Khan

  4. School of Engineering, Macquarie University, Sydney, Australia

    Yinan Kong

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  1. Tariq M. Khan
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  2. Donald G. Bailey
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Khan, T.M., Bailey, D.G., Khan, M.A.U. et al. Real-time iris segmentation and its implementation on FPGA. J Real-Time Image Proc 17, 1089–1102 (2020). https://doi.org/10.1007/s11554-019-00859-w

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