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Experimental analysis regarding the influence of iris segmentation on the recognition rate

Experimental analysis regarding the influence of iris segmentation on the recognition rate

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In this study the authors will look at the detection and segmentation of the iris and its influence on the overall performance of the iris-biometric tool chain. The authors will examine whether the segmentation accuracy, based on conformance with a ground truth, can serve as a predictor for the overall performance of the iris-biometric tool chain. That is: If the segmentation accuracy is improved will this always improve the overall performance? Furthermore, the authors will systematically evaluate the influence of segmentation parameters, pupillary and limbic boundary and normalisation centre (based on Daugman's rubbersheet model), on the rest of the iris-biometric tool chain. The authors will investigate if accurately finding these parameters is important and how consistency, that is, extracting the same exact region of the iris during segmenting, influences the overall performance.


    1. 1)
      • 43. Du, Y., Bonney, D., Ives, R., et al: ‘Analysis of partial iris recognition using a 1d approach’. Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 2005), 2005, vol. 2, pp. 1823.
    2. 2)
      • 32. Alonso-Fernandez, F., Bigun, J.: ‘Iris boundaries segmentation using the generalized structure tensor. an study on the effects of image degradation.’. Proc. of the IEEE Conf. on Biometrics: Theory, Applications and Systems (BTAS), 2012.
    3. 3)
    4. 4)
      • 18. Bowyer, K., Hollingsworth, K., Flynn, P.: ‘A survey of iris biometrics research: 2008–2010’, in Burge, M.J., Bowyer, K.W. (Eds.): ‘Handbook of Iris Recognition, ser. Advances in computer vision and pattern recognition(Mark J. Burge and Kevin W. Bowyer eds) (Springer, London, 2013), pp. 1554, doi: 10.1007/978-1-4471-4402-1_2.
    5. 5)
    6. 6)
      • 31. Uhl, A., Wild, P.: ‘Multi-stage visible wavelength and near infrared iris segmentation framework’. Proc. of the Int. Conf. on Image Analysis and Recognition (ICIAR'12), ser. LNCS, Aveiro, Portugal, 2012, pp. 110.
    7. 7)
      • 6. Zuo, N., Schmid, N.A.: ‘An automatic algorithm for evaluation of precision of iris segmentation’. Second IEEE Int. Conf. on Biometrics: Theory, Applications and Systems (BTAS 2008), October 2008, pp. 16.
    8. 8)
      • 21. Rathgeb, C., Uhl, A., Wild, P.: ‘Effects of severe image compression on iris segmentation performance (best poster award)’. Proc. of the IAPR/IEEE Int. Joint Conf. on Biometrics (IJCB'14), 2014.
    9. 9)
      • 26. ND-0405 iris image dataset, available at
    10. 10)
      • 4. Tabassi, E., Grother, P., Salamon, W.: ‘IREX II – iris quality calibration and evaluation’. National Institute of Standards and Technology (NIST), Technical Report NIST Interagency Report 7296, 2011.
    11. 11)
      • 7. Alonso-Fernandez, F., Bigun, J.: ‘Quality factors affecting iris segmentation and matching’.  2013 Int. Conf. on Biometrics (ICB), 2013, pp. 16. doi: 10.1109/ICB.2013.6613016.
    12. 12)
      • 23. Iris segmentation ground truth database – elliptical/polynomial boundaries (IRISSEG-EP), available at
    13. 13)
      • 38. van Rijsbergen, C.J.: ‘Information retrieval’ (Butterworth-Heinemann, 1979, 2nd edn.).
    14. 14)
      • 15. Jeong, D.S., Hwang, J.W., Kang, B.J., et al: ‘A new iris segmentation method for non-ideal iris images’, Image Vis. Comput., 2010, 28, (2), pp. 254260, available at
    15. 15)
      • 30. Petrovska, D., Mayoue, A.: ‘Description and documentation of the biosecure software library’, Project No IST-2002-507634 - BioSecure, Deliverable, 2007.
    16. 16)
      • 47. Proença, H., Alexandre, L.A.: ‘Iris recognition: analysis of the error rates regarding the accuracy of the segmentation stage’, Image Vis. Comput., 2010, 28, (1), pp. 202206, available at
    17. 17)
    18. 18)
    19. 19)
      • 22. Hofbauer, H., Alonso-Fernandez, F., Wild, P., et al: ‘A ground truth for iris segmentation’. Proc. 22nd Int. Conf. on Pattern Recognition (ICPR'14), Stockholm, Sweden, 2014, p. 6.
    20. 20)
      • 36. Alonso-Fernandez, F., Tome-Gonzalez, P., Ruiz-Albacete, V., et al: ‘Iris recognition based on sift features’. 2009 Int. Conf. on Biometrics, Identity and Security (BIdS), 2009, pp. 18. doi: 10.1109/BIDS.2009.5507529.
    21. 21)
    22. 22)
      • 28. USIT – University of Salzburg iris toolkit, available at, version 1.0.x.
    23. 23)
      • 44. Islam, M., Wang, Y.C., Khatun, A.: ‘Partial iris image recognition using wavelet based texture features’. 2010 Int. Conf. on Intelligent and Advanced Systems (ICIAS), 2010.
    24. 24)
      • 45. Rathgeb, C., Uhl, A., Wild, P.: ‘Incremental iris recognition: a single-algorithm serial fusion strategy to optimize time complexity’. Proc. Fourth IEEE Int. Conf. on Biometrics: Theory, Application, and Systems 2010 (IEEE BTAS'10), Washington DC, DC, USA, September 2010, pp. 16.
    25. 25)
      • 5. Benini, D., et al: ‘ISO/IEC 29794-6 biometric sample quality – part 6: iris image data’. International Organization for Standardization, Technical Report JTC1/SC37/Working Group 3, 2012.
    26. 26)
      • 19. Hämmerle-Uhl, J., Tillian, E., Uhl, A.: ‘Recognition impact of JPEG2000 Part 2 wavelet packet subband structures in IREX K3 iris image compression’. Int. J. of Information and Electronics Engineering (Proc. of ICSIA'14), 2015, vol. 5, no. 1, pp. 5154.
    27. 27)
      • 25. IIT Delhi iris database, available at
    28. 28)
      • 29. Uhl, A., Wild, P.: ‘Weighted adaptive hough and ellipsopolar transforms for real-time iris segmentation’. Proc. Fifth IAPR/IEEE Int. Conf. on Biometrics (ICB'12), New Delhi, India, March 2012, pp. 18.
    29. 29)
      • 16. Pundlik, S., Woodard, D., Birchfield, S.: ‘Iris segmentation in non-ideal images using graph cuts’, Image Vis. Comput., 2010, 28, (12), pp. 16711681, available at
    30. 30)
      • 27. Rathgeb, C., Uhl, A., Wild, P.: ‘Iris recognition: from segmentation to template security, ser. advances in information security’ (Springer Verlag, 2013), vol. 59.
    31. 31)
    32. 32)
      • 35. Rathgeb, C., Uhl, A.: ‘Secure iris recognition based on local intensity variations’. Proc. of the Int. Conf. on Image Analysis and Recognition (ICIAR'10), ser. Springer LNCS, 6112, Povoa de Varzim, Portugal, June 2010, pp. 266275.
    33. 33)
      • 20. Bergmüller, T., Christopoulos, E., Schnöll, M., et al: ‘Recompression effects in iris segmentation’. Proc. Eighth IAPR/IEEE Int. Conf. on Biometrics (ICB'15), Phuket, Thailand, May 2015, pp. 18.
    34. 34)
    35. 35)
    36. 36)
      • 12. Wildes, R.P.: ‘Iris recognition: an emerging biometric technology’. Proc. IEEE, 1997, vol. 85, pp. 13481363.
    37. 37)
      • 39. Hofbauer, H., Uhl, A., Unterweger, A.: ‘Transparent encryption for HEVC using bit-stream-based selective coefficient sign encryption’. 2014 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP) IEEE, Florence, Italy, May 2014, pp. 19861990.
    38. 38)
      • 46. Rathgeb, C., Uhl, A., Wild, P.: ‘On combining selective best bits of iris-codes’. Proc. of the Biometrics and ID Management Workshop (BioID'11), Vielhauer, C., Dittmann, J., Drygajlo, A., Fairhust, M. (Eds.): ser. Springer LNCS, Brandenburg on the Havel, Germany, March. 2011, vol. 6583, pp. 227237.
    39. 39)
    40. 40)
    41. 41)
    42. 42)
    43. 43)
      • 24. CASIA-interval version 4 iris database, available at
    44. 44)
      • 33. Masek, L.: ‘Recognition of Human Iris Patterns for Biometric Identification’. Master's thesis, University of Western Australia, 2003.
    45. 45)
      • 40. Wild, P., Hofbauer, H., Ferryman, J., Uhl, A.: ‘Segmentation-level Fusion for Iris Recognition’. Proc. Int. Conf. Biometrics Special Interest Group (BIOSIG’15), p. 12, Darmstadt, Germany, September 9–11.
    46. 46)
      • 10. Erbilek, M., Abreu, M.C.D.C., Fairhurst, M.: ‘Optimal configuration strategies for iris recognition processing’. IET Conf. on Image Processing (IPR 2012), 2012, 6, p. 2.
    47. 47)

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