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access icon free Impact of (segmentation) quality on long vs. short-timespan assessments in iris recognition performance

Several researchers have presented studies of temporal effects on iris recognition accuracy, with varying results on severity of observed effects. The sensitive topic continues to be adversely discussed and the difficulty of isolating performance-impacting factors is immanent. The impact of ageing on segmentation vs. feature extraction has been largely neglected so far. This study attempts to shed light on the impact of segmentation quality on observed temporal effects highlighting the critical role of the segmentation module and quality assessment when assessing ageing effects. The lack of large and standardised temporal variation in public datasets as well as additional metadata (age of subject, recording parameters) and strictly enforced unified recording and quality guidelines over time-separated sessions are identified as imminent problems of ageing studies. Results are reported on a long-timespan database of 36,240 images comprising 104 classes and a 4 year time lapse, offering a large variety of recording conditions highlighting the critical role of a transparent recording setup.

References

    1. 1)
      • 4. Grother, P., Matey, J.R., Tabasi, E., Quinn, G.W., Chumakov, M.: ‘IREX VI temporal stability of iris recognition accuracy’. Interagency Report, 7948, NIST, 2013.
    2. 2)
      • 27. Hofbauer, H., Alonso-Fernandez, F., Wild, P., Bigun, J., Uhl, A.: ‘A ground truth for iris segmentation’. Proc. 22nd Int. Conf. on Pattern Recognition (ICPR), Stockholm, Sweden, August 2014, pp. 16.
    3. 3)
    4. 4)
    5. 5)
      • 3. Baker, S.E., Bowyer, K.W., Flynn, P.J.: ‘Empirical evidence for correct iris match score degradation with increased time-lapse between gallery and probe matches’. Proc. Int. Conf. on Biometrics (ICB), Alghero, Italy, June 2009, pp. 11701179.
    6. 6)
      • 24. Masek, L.: ‘Recognition of human iris patterns for biometric identification’. Master's thesis, University of Western Australia, 2003.
    7. 7)
      • 18. Grother, P., Matey, J., Quinn, G., Tabassi, E.: ‘Iris permanence’. What We Know, What We Don't, and How to Find Out More. Presentation at Global Identity Summit, Iris Workshop, Tampa, USA, September 2014, p. 17, Retrieved from http://www.biometrics.org/bc2014/presentations/Tues_1819_Grother_1400.pdf.
    8. 8)
    9. 9)
    10. 10)
    11. 11)
      • 26. Xiao, L., Sun, Z., He, R., Tan, T.: ‘Coupled feature selection for cross-sensor iris recognition’. Proc. Int. Conf. Biometrics: Theory, Applications, and Systems (BTAS), Washington DC, USA, September 2013, pp. 16.
    12. 12)
      • 16. Czajka, A.: ‘Template ageing in iris recognition’. Proc. Int. Conf. on Bio-Inspired Systems and Signal Processing (BISSP), Barcelona, Spain, February 2013, pp. 18.
    13. 13)
      • 5. Fenker, S.P., Bowyer, K.W.: ‘Analysis of template aging in iris biometrics’. Proc. IEEE Computer Vision and Pattern Recognition Workshop (CVPRW), Providence, RI, USA, June 2012, pp. 4551.
    14. 14)
      • 6. Bowyer, K.W., Ortiz, E.: ‘Making sense of the IREX VI report’. Cvrl Technical Report, University of Notre Dame, 2013.
    15. 15)
      • 14. Tome-Gonzalez, P., Alonso-Fernandez, F., Ortega-Garcia, J.: ‘On the effects of time variability in iris recognition’. Proc. Int. Conf. Biometrics: Theory, Applications, and Systems (BTAS), Arlington, VA, USA, September 2008, pp. 16.
    16. 16)
      • 17. Sgroi, A., Bowyer, K.W., Flynn, P.J.: ‘The prediction of young and old subjects from iris texture’. Proc. Int. Conf. on Biometrics (ICB), Madrid, Spain, June 2013, pp. 15.
    17. 17)
    18. 18)
    19. 19)
    20. 20)
      • 20. Trokielewicz, M.: ‘Linear regression analysis of template aging in iris recognition’. Third Int. WS on Biometrics and Forensics, Gjovic, Norway, March 2015, pp. 16.
    21. 21)
    22. 22)
      • 19. Ortiz, E., Bowyer, K.W., Flynn, P.J.: ‘A linear regression analysis of the effects of age related pupil dilation change in iris biometrics’. Proc. Int. Conf. on Biometrics: Theory, Applications, and Systems (BTAS), Washington DC, USA, September 2013, pp. 16.
    23. 23)
    24. 24)
      • 25. Uhl, A., Wild, P.: ‘Weighted adaptive Hough and ellipsopolar transforms for real-time iris segmentation’. Proc. Int. Conf. on Biometrics (ICB), New Delhi, India, March 2012, pp. 18.
    25. 25)
      • 15. Sazonova, N., Hua, F., Liu, X., et al: ‘A study on quality-adjusted impact of time lapse on iris recognition’. Proc. SPIE 8371, 2012, pp. 83711W9.
    26. 26)
    27. 27)
      • 22. Rathgeb, C., Uhl, A., Wild, P.: ‘Iris biometrics: from segmentation to template security’ (Springer, New York, NY, 2013).
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