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Robust iris location in close-up images of the eye

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Abstract

This paper presents a robust, reliable iris location system for close-up, grey scale images of a single eye. The system is meant as a bootstrap or recovery module for automated iris tracking within medical applications. We model the iris contour with an active ellipse, sensitive to intensity gradients across its perimeter. In this way, we avoid modelling the noisy appearance of the iris (e.g. corneal reflections). The iris–sclera intensity transition is modelled at two spatial scales with Petrou–Kittler optimal ramp filters. The optimal ellipse is identified by a simulated annealing algorithm tuned to the problem characteristics. The system performed accurately and robustly with 327 real images against substantial occlusion levels and varying image quality, subject, eye shape and skin colour.

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References

  1. Blake A, Yuille AL (1992) Active vision. MIT, Cambridge

    Google Scholar 

  2. Duchowsky AT (2003) Eye tracking methodology. Springer, Berlin Heidelberg New York

    Google Scholar 

  3. Bettinger F, Cootes TF, Taylor CJ (2002) Modelling facial behaviours. In: Proceedings. BMVC’02, vol 2, pp 797–806

  4. Luo B, Hancock ER (2001) Structural graph matching using the EM algorithm and simulated annealing. IEEE Trans PAMI 23(10)

    Google Scholar 

  5. Morimoto CH, Koons D, Amir A, Flickner M (2000) Pupil detection and tracking using multiple light sources. Image Vis Comput

    Google Scholar 

  6. Cristinacce D, Cootes TF (2003) Facial feature detection using ADABOOST with shape constraints. In: Proceedings of BMVC’03, 1:231–240

  7. Daugman J (2004) How iris recognition works. IEEE Trans Cir syst video technol 14(1)

    Google Scholar 

  8. Davis RH, Twining C, Cootes T, Taylor C (2002) A minimum description length approach to statistical shape modelling. IEEE Trans Medical Imaging 21:535–527

    Google Scholar 

  9. Tan HL, Gelfand SB, Delp EJ (1992) A cost minimization approach to edge detection using simulated annealing. IEEE Trans PAMI 14:3–18

    Google Scholar 

  10. Isard M, Blake A (1998) Active contours. Springer,Berlin Heidelberg New York

    Google Scholar 

  11. Daugman J (1993) High confidence visual recognition of persons by a test of statistical independence. IEEE Trans PAMI 15(11)

    Google Scholar 

  12. Ivins JP, Porrill J, Frisby JP (1997) A deformable model of the human iris driven by non-linear least-squares minimisation. In: Proceedigns of IPA’97, 1:234–238

  13. Weber J, Malik J (1994) Robust computation of optical flow in a multi-scale differential framework. Int J Comput Vis

  14. Kerrigan S, McKenna S, Ricketts IW, Widgerowitz C (2003) Analysis of total hip replacements using active ellipses. In: Proceedings of Medical Images Understanding and Analysis (MIUA)

  15. Leroy B, Medioni GG, Johnson E, Matthies L (2001) Crater detection for autonomous landing on asteroids. Image Vis Comput 19(11):787–792

    Article  Google Scholar 

  16. Ingber L (1989) Very fast simulated re-annealing. Mathematical and computer modelling 12:967–973

    Article  MATH  MathSciNet  Google Scholar 

  17. Ma L, Tan T, Wang Y, Zhang D (2003) Personal identification based on iris texture analysis. IEEE Trans PAMI 25(12)

    Google Scholar 

  18. Petrou M, Kittler J (1991) Optimal edge detectors for ramp edges. IEEE Trans. PAMI 13

  19. Vincze M, Ayromlou M, Zillich M (2000) Fast tracking of ellipses using edge-projected integration of cues. In: International Conference on Pattern Recognition (ICPR’00)

  20. Ritter N, Owens R, Cooper J, Eikelboom R, van Saarlos PP (1999) Registration of stereo and temporal images of the retina. IEEE Trans Med Imaging 18(5)

    Article  Google Scholar 

  21. Salamon P, Sibani P, Frost R (2002) Facts, Conjectures and Improvements for Simulated Annealing. SIAM

  22. Shih S-W, Liu J (2004) A novel approach to 3-d gaze tracking using stereo cameras. IEEE SMC (Part B) 34(1):234–245

    Google Scholar 

  23. Ramadan S, Abd-Almageed W, Smith CE (2002) Eye tracking using active deformable models. In: Proceedings of ICVGIP’02

  24. Richard S (2004) Comparative experimental assessment of four optimisation algorithms applied to iris location. Master’s Thesis, ECE/EPS, Heriot-Watt University

  25. Cornsweet TN, Crane HD (1973) Accurate two-dimensional eye tracker using first and fourth Purkinije images. J Opt Soc Am 63(8):921–928

    Article  PubMed  Google Scholar 

Download references

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Authors and Affiliations

  1. Vision, Signal and Image Processing Research Group School of Engineering and Physical Sciences, Heriot Watt University, Riccarton, Edinburgh, EH14 4AS, UK

    Emanuele Trucco & Marco Razeto

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  1. Emanuele Trucco
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  2. Marco Razeto
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Correspondence to Emanuele Trucco.

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Trucco, E., Razeto, M. Robust iris location in close-up images of the eye. Pattern Anal Applic 8, 247–255 (2005). https://doi.org/10.1007/s10044-005-0004-8

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  • DOI: https://doi.org/10.1007/s10044-005-0004-8

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