Abstract
Using a data set with approximately 4 years of elapsed time between the earliest and most recent images of an iris (23 subjects, 46 irises, 6,797 images), we investigate template aging for iris biometrics. We compare the match and non-match distributions for short-time-lapse image pairs, acquired with no more than 120 days of time lapse between them, to the distributions for long-time-lapse image pairs, with at least 1,200 days of time lapse. We find no substantial difference in the non-match, or impostor, distribution between the short-time-lapse and the long-time-lapse data. We do find a difference in the match, or authentic, distributions. For the image dataset and iris biometric systems used in this work, the false reject rate increases by about 50 % or greater for the long-time-lapse data relative to the short-time-lapse data. The magnitude of the increase in the false reject rate varies with changes in the decision threshold, and with different matching algorithms. Our results demonstrate that iris biometrics is subject to a template aging effect.
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Acknowledgments
SEB, KWB, and PJF were supported by the National Science Foundation under grant CNS01-30839, by the Central Intelligence Agency, by the Intelligence Advanced Research Projects Activity and by the Technical Support Working Group under US Army contract W91CRB-08-C-0093. PJP acknowledges the support of the the Biometric Task Force, the Department of Homeland Security’s Directorate for Science and Technology, the Intelligence Advanced Research Projects Activity (IARPA), the Federal Bureau of Investigation (FBI), and the Technical Support Working Group (TSWG).
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Baker, S.E., Bowyer, K.W., Flynn, P.J., Phillips, P.J. (2016). Template Aging in Iris Biometrics. In: Bowyer, K., Burge, M. (eds) Handbook of Iris Recognition. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6784-6_23
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DOI: https://doi.org/10.1007/978-1-4471-6784-6_23
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