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 data set 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.

Using a data set with approximately four 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 nonmatch 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 longtime-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.

Many iris recognition systems operate under the assumption that non-cosmetic contact lenses have no or minimal effect on iris biometrics performance and convenience. In this paper we show results of a study of 12,003 images from 87 contact-lens-wearing subjects and 9697 images from 124 non-contact-lens-wearing subjects. We visually classified the contact lens images into four categories according to the type of lens effects observed in the image. Our results show different degradations in performance for different types of contact lenses. Lenses that produce larger artifacts on the iris yield more degraded performance. This is the first study to document degraded iris biometrics performance with non-cosmetic contact lenses.

It is widely assumed that, barring traumatic injury to the eye or eye surgery, a person's iris does not change over time.This implies that iris recognition performance does not degrade as time increases after initial enrollment-also known as the template-aging problem.We present results of the first study to investigate the validity of this assumption.We explore the effects of time lapse since enrollment on iris biometric match scores using a data set with four years time lapse between the earliest and most recent images of an iris (23 subjects, 46 irises, 6,814 total images).Experimental results are reported for three iris recognition algorithms.We find that the empirical mean of the distribution for similarity scores between iris images of the same person changes over four years-in biometric parlance, the match or genuine distribution changes.The change is statistically significant when comparing the mean of pairs of images taken within 120 days (∼4 months) and over 1200 days (∼3.25 years) for the three algorithms in our study.The mean of the match distribution changes so that the expected performance would degrade due to an increase in the false reject rate.Our results suggest that iris biometric templates undergo aging.

We explore the effects of time lapse on iris biometrics using a data set of images with four years time lapse between the earliest and most recent images of an iris (13 subjects, 26 irises, 1809 total images). We find that the average fractional Hamming distance for a match between two images of an iris taken four years apart is statistically significantly larger than the match for images with only a few months time lapse between them. A possible implication of our results is that iris biometric enrollment templates may undergo aging and that iris biometric enrollment may not be "once for life." To our knowledge, this is the first and only experimental study of iris match scores under long (multi-year) time lapse.

We consider three “accepted truths” about iris biometrics, involving pupil dilation, contact lenses and template aging. We also consider a relatively ignored issue that may arise in system interoperability. Experimental results from our laboratory demonstrate that the three accepted truths are not entirely true, and also that interoperability can involve subtle performance degradation. All four of these problems affect primarily the stability of the match, or authentic, distribution of template comparison scores rather than the non-match, or imposter, distribution of scores. In this sense, these results confirm the security of iris biometrics in an identity verification scenario. We consider how these problems affect the usability and security of iris biometrics in large-scale applications, and suggest possible remedies.

Many security applications require accurate identification of people, and research has shown that iris biometrics can be a powerful identification tool. However, in order for iris biometrics to be used on larger populations, error rates in the iris biometrics algorithms must be as low as possible. Furthermore, these algorithms need to be tested in a number of different environments and configurations. In order to facilitate such testing, we have collected more than 100,000 iris images for use in iris biometrics research. Using this data, we have developed a number of techniques for improving recognition rates. These techniques include fragile bit masking, signal-level fusion of iris images, and detecting local distortions in iris texture. Additionally, we have shown that large degrees of dilation and long lapses of time between image acquisitions negatively impact performance.

Many iris recognition systems operate under the assumption that non-cosmetic contact lenses will not affect match quality and the convenience using iris biometrics. We show results opposing this belief with a study of 51 contact lens wearing subjects and 64 non contact lens wearing subjects. Our experimental results show that contacts lens wearers are 14 times more likely to be falsely rejected by the IrisBEE iris recognition system at a Hamming distance threshold of 0.32 than non contact lens wearers. We further classify contact lens wearers into four categories according to the type of lens and its visibility in the iris image. The false reject rate varies with different types of contacts and the artifacts they produce on iris images. This is the first work that we are aware of to look at the effects of prescription contact lenses on iris biometrics.