Elastic strips normalisation model for higher iris recognition performance

Elastic strips normalisation model for higher iris recognition performance

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Iris recognition is among the best biometric systems. Characterised by the iris's uniqueness, universality, distinctiveness, permanence and collectability, the iris recognition system achieves high performance and real time response. In this study, the authors propose an improved iris normalisation model applied after a precise iris segmentation process. The normalisation model defines a new reference space for iris features. It normalises the iris using radial strips with a shape that changes between the pupil's boundary and the circular approximation of the iris's outer boundary. Moreover, the effect of the centres of the normalisation strips is evaluated by assessing the recognition performance when comparing three different centres configurations. The approach is tested on 2491 images from the CASIA V3 database. The system's performance is measured at the matching stage. Higher decidability and recognition accuracy at equal error rate is obtained. Detection error tradeoff curves are estimated by using the proposed model and compared with Daugman's reference system for assessing performance improvement.


    1. 1)
      • 1. Jain, A.K., Ross, A., Prabhakar, S.: ‘An introduction to biometric recognition’, IEEE Trans. Circuits Syst. Video Technol., 2004, 14, (1), pp. 420 (doi: 10.1109/TCSVT.2003.818349).
    2. 2)
      • 2. Sanderson, S., Erbetta, J.H.: ‘Authentication for secure environments based on iris scanning technology’. IEE Colloquium on Visual Biometrics, London, 2000, pp. 8/18/7.
    3. 3)
      • 3. Seiberg, D.: ‘Iris recognition at airports uses eye-catching technology’, Technology Editor, July 2000, accessed December 2012.
    4. 4)
      • 4., accessed July 2013.
    5. 5)
      • 5. Krichen, E.: ‘Reconnaissance des personnes par l'iris en mode dégradé’, PhD thesis, Evry-Val Essonne University, 2007.
    6. 6)
      • 6. Daugman, J.G.: ‘High confidence visual recognition of persons by a test of statistical independence’, IEEE Trans. Pattern Anal. Mach. Intell., 1993, 15, (11), pp. 11481161 (doi: 10.1109/34.244676).
    7. 7)
      • 7. Wildes, R.P.: ‘Iris recognition: an emerging biometric technology’, Proc. IEEE, 1997, 85, (9), pp. 13481363 (doi: 10.1109/5.628669).
    8. 8)
      • 8. Ma, L., Tan, T., Wang, Y., Zhang, D.: ‘Personal identification based on iris texture analysis’, IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25, (12), pp. 15191533 (doi: 10.1109/TPAMI.2003.1251145).
    9. 9)
      • 9. He, Z., Tan, T., Sun, Z., Qiu, X.: ‘Toward accurate and fast iris segmentation for iris biometrics’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, (9), pp. 16701684 (doi: 10.1109/TPAMI.2008.183).
    10. 10)
      • 10. Ma, L., Tan, T., Wang, Y., Zhang, D.: ‘Local intensity variation analysis for iris recognition’, Pattern Recognit. Soc., 2004, 37, (6), pp. 12871298 (doi: 10.1016/j.patcog.2004.02.001).
    11. 11)
      • 11. Tan, T., He, Z., Sun, Z.: ‘Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition’, Image Vis. Comput., 2010, 28, (2), pp. 223230 (doi: 10.1016/j.imavis.2009.05.008).
    12. 12)
      • 12. Weiqi, Y., Lu, X., Zhonghua, L.: ‘A novel iris localization algorithm based on the gray distributions of eye images’. IEEE-EMBS 27th Annual Int. Conf. of the Engineering in Medicine and Biology Society, Shanghai, China, January 2006, pp. 65046507.
    13. 13)
      • 13. Zuo, J., Schmid, N.A.: ‘On a methodology for robust segmentation of nonideal iris images’, IEEE Trans. Syst. Man Cybern. B, Cybern., 2010, 40, (3), pp. 703718 (doi: 10.1109/TSMCB.2009.2015426).
    14. 14)
      • 14. Daugman, J.: ‘New methods in iris recognition’, IEEE Trans. Syst. Man Cybern. B, Cybern., 2007, 37, (5), pp. 11671175 (doi: 10.1109/TSMCB.2007.903540).
    15. 15)
      • 15. Shah, S., Ross, A.: ‘Iris segmentation using geodesic active contours’, IEEE Trans. Inf. Forensics Sec., 2009, 4, (4), pp. 824836 (doi: 10.1109/TIFS.2009.2033225).
    16. 16)
      • 16. Chen, R., Lin, X., Ding, T., Ma, J.: ‘Accurate and fast iris segmentation applied to portable image capture device’. IEEE Int. Workshop on Imaging Systems and Techniques, Shenzhen, China, 2009, pp. 8084.
    17. 17)
      • 17. Hilal, A., Daya, B., Beauseroy, P.: ‘Hough transform and active contour for enhanced iris segmentation’, Int. J. Comput. Sci. Issues, 2012, 9, (6), pp. 110.
    18. 18)
      • 18. Hilal, A., Beauseroy, P., Daya, B.: ‘Real shape inner iris boundary segmentation using active contour without edges’. Int. Conf. on Audio, Language and Image Processing, Shanghai, China, July 2012, pp. 1419.
    19. 19)
      • 19. Daugman, J.: ‘How iris recognition works’, IEEE Trans. Circuits Syst. Video Technol., 2004, 14, (1), pp. 2130 (doi: 10.1109/TCSVT.2003.818350).
    20. 20)
      • 20. Chan, T.F., Vese, L.A.: ‘Active contours without edges’, IEEE Trans. Image Process., 2001, 10, (2), pp. 266277 (doi: 10.1109/83.902291).
    21. 21)
      • 21. Boles, W.W., Boashash, B.: ‘A human identification technique using images of the iris and wavelet transform’, IEEE Trans. Signal Process., 1998, 46, (4), pp. 11851188 (doi: 10.1109/78.668573).
    22. 22)
      • 22. Shamsi, M., Rasouli, A.: ‘An innovative trapezium normalization for iris recognition systems’. Int. Conf. on Computer and Software Modeling, Singapore, 2011, vol. 14.
    23. 23)
      • 23. Hilal, A., Daya, B., Beauseroy, P.: ‘Improved iris recognition using parabolic normalization and multi-layer perceptron neural network’. Proc. Fourth Int. Joint Conf. on Computational Intelligence, Barcelona, Spain, October 2012, pp. 643646.
    24. 24)
      • 24. CASIA-IrisV3,, accessed December 2012.

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