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Quantitative evaluation of the effects of aging on biometric templates

Quantitative evaluation of the effects of aging on biometric templates

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The long-term performance of biometric authentication systems is highly depended on the permanence of biometric features stored in biometric templates. Aging variation causes modifications on biometric features that affect the matching between stored and captured biometric templates causing in that way deterioration in the performance of biometric authentication systems. In this study the authors attempt to quantify the effects of aging for different biometric modalities, so that it is possible to draw conclusions related to the effect of aging on different types of biometric templates. In this context variations between distributions containing biometric features from different age groups are quantified, allowing in that way the definition of age-sensitive and age-invariant biometric features. An important aspect of the proposed approach is the standardised and generic nature of the approach that allows the derivation of comparative results between different modalities and different feature vectors. The work presented in this study provides a valuable tool for selecting, either age-invariant features for use in identity authentication applications, or for selecting age-sensitive features for age-estimation-related applications.

References

    1. 1)
      • K. Matsuo , F. Okumura , M. Hashimoto , S. Sakazawa , Y. Hatori . Arm swing identification method with template update for long term stability.
    2. 2)
      • A. Rattani , B. Freni , G. Marcialis , F. Roli . Template update methods in adaptive biometric systems: a critical review.
    3. 3)
      • F. Roli , L. Didaci , G. Marcialis . (2007) Template Co-update in multimodal biometric systems.
    4. 4)
    5. 5)
    6. 6)
    7. 7)
      • 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', Third IAPR/IEEE Int. Conf. on Biometrics, 2009, p. 1170–1179, (LNCS, 5558).
    8. 8)
    9. 9)
      • Modi, S.K., Elliott, S.J., Whetsone, J., Hakil, K.: `Impact of age groups on fingerprint recognition performance', IEEE Workshop on Automatic Identification Advanced Technologies, 2007, p. 19–23.
    10. 10)
      • W.W. Greulich , S.I. Pyle . (1959) Radiographic Atlas of skeletal development of the hand and wrist.
    11. 11)
      • Linville, S.E.: `The aging voice', The ASHA Leader, 2004, p. 21, (available at http://www.asha.org/Publications/leader/2004/041019/041019e.htm, last accessed March 2011).
    12. 12)
      • L. Larsson , G. Grimby , J. Karsson . Muscle strength and speed of movement in relation to age and muscle morphology. J. Appl. Physiol. , 3 , 451 - 456
    13. 13)
      • Latifi, S., Solayappan, N.: `A survey of unimodal biometric methods', Proc. 2006 Int. Conf. on Security and Management, 2006, p. 57–63.
    14. 14)
      • A.A. Ross , K. Nandakumar , A.K. Jain . (2006) Handbook of multibiometrics.
    15. 15)
      • Min, J., Flynn, P.J., Bowyer, K.W.: `Assessment of time dependency in face recognition', Fourth Int. Conf. on Audio and Video-Based Biometric Person Authentication (AVBPA), 2003, p. 44–51, (LNCS, 2688).
    16. 16)
      • N. Poh , J. Kittler , R. Smith , J.R. Tena . A method for estimating authentication performance over time, with applications to face biometrics.
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
    23. 23)
      • A.K. Jain , P. Flynn , A.A. Ross . (2008) Handbook of biometrics.
    24. 24)
      • J.D. Woodward , N.M. Orlans , P.T. Higgins . (2002) Biometrics: identity assurance in the information age.
    25. 25)
    26. 26)
      • Zaphiris, P., Ellis, R.D.: `Mathematical modeling of age differences in hierarchical information systems', Proc. ACM Conf. on Universal Usability, 2000, p. 157–158.
    27. 27)
      • Uhl, A., Wild, P.: `Comparing verification performance of kids and adults for fingerprint, palmprint, hand-geometry and digitprint biometrics', Proc. Third IEEE Int. Conf. on Biometrics: Theory, Application, and Systems, 2009, p. 1–6.
    28. 28)
    29. 29)
    30. 30)
      • Ricanek, K., Tesafaye, T.: `MORPH a longitudinal image database of normal adult age-progression', Proc. Seventh IEEE Int. Conf. on Automatic Face and Gesture Recognition, 2006, p. 341–345.
    31. 31)
      • Ling, H., Soatto, S., Ramanathan, N., Jacobs, D.W.: `A study of face recognition as people age', Proc. IEEE 11th Int. Conf. on Computer Vision, 2007, p. 1–8.
    32. 32)
      • S. Theodoridis , K. Koutroumbas . (2009) Pattern recognition.
    33. 33)
    34. 34)
      • Lanitis, A.: `Comparative evaluation of automatic age progression methodologies', EURASIP J. Adv. Signal Process., 2008, 2008, doi: 10.1155/2008/239480.
    35. 35)
    36. 36)
    37. 37)
      • Hill, C.M., Solomon, C.J., Gibson, S.J.: `Aging the human face – a statistically rigorous approach', IEE Int. Symp. on Imaging for Crime Detection and Prevention, 2005, p. 89–94.
    38. 38)
      • Lanitis, A.: `On the significance of different facial parts for age estimation', Proc. 14th Int. Conf. on Digital Signal Processing, 2002, p. 1027–1031.
    39. 39)
      • ISO/IEC 15938-3:2001: ‘Information technology – multimedia content description interface – part 3: visual’, ver. 1.
    40. 40)
      • ISO/IEC/JTC1/SC29/WG11: ‘MPEG-7 visual experimentation model (XM)’, version 10.0, Doc. N4063, March 2001.
    41. 41)
    42. 42)
      • Tsapatsoulis, N., Theodosiou, Z.: `Object classification using the MPEG-7 visual descriptors: an experimental evaluation using state of the art data classifiers', Proc. Artificial Neural Networks – ICANN, 2009, p. 905–912, (LNCS, 5769).
    43. 43)
      • I.H. Witten , E. Frank . (2000) Data mining: practical machine learning tools and techniques with java implementations.
    44. 44)
      • F. Frangeskides , A. Lanitis . (2007) Multi-modal contact-less human computer interaction.
    45. 45)
      • Lanitis, A.: `Age estimation based on head movements: a feasibility study', Fourth Int. Symp. on Communications, Control and Signal Processing, 2010.
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