Template Aging Phenomenon in Iris Recognition

Fenker, S. P.; Ortiz, Estefan; Bowyer, Kevin W.

doi:10.1109/access.2013.2262916

Cited by 26 publications

(16 citation statements)

References 30 publications

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“…Iris template aging studies that compare, for the same set of subjects, the false non-match rate for short-time-lapse matches versus the false non-match rate (FNMR) for longtime-lapse matches, find an increased FNMR with additional elapsed time [2], [10]. This basic result has since been reported by other research groups analyzing other datasets.…”

Section: B Scientific Knowledge and Technical Advancementmentioning

confidence: 76%

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Lessons from collecting a million biometric samples

Flynn

Bowyer

Phillips

2015

2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)

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Over the past decade, independent evaluations have become commonplace in many areas of experimental computer science, including face and gesture recognition. A key attribute of many successful independent evaluations is a curated data set. Desired things associated with these data sets include appropriateness to the experimental design, a corpus size large enough to allow statistically rigorous characterization of results, and the availability of comprehensive metadata that allow inferences to be made on various data set attributes. In this paper, we review a ten-year biometric sampling effort that enabled the creation of several key biometrics challenge problems. We summarize the design and execution of data collections, identify key challenges, and convey some lessons learned.

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Section: B Scientific Knowledge and Technical Advancementmentioning

confidence: 76%

“…Because of the continual change in the student population, there are constraints on the size of longitudinal studies readily supported [2], [10], [11]. The longest time lapse between a subject's first and last acquisition was 3666 days (10 years).…”

Section: Overview Of Data Collectionmentioning

confidence: 99%

Lessons from collecting a million biometric samples

Flynn

Bowyer

Phillips

2015

2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)

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“…They found the evidence for the effect of template aging and concluded that the changes will be visible at one year and increases with an increase in time. In this analysis, they had noticed that more than 50% of false matches were found with two years' time-lapse whereas near about 150% with three years lapse as investigated [12].…”

Section: Effect Of Template Aging On Iris Based Biometric Systemsmentioning

confidence: 78%

EAgeBioS: Enhanced Biometric System to handle the Effects of Template Aging

Kumar*,

Lamba,

Jangra

2019

IJITEE

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Biometric Systems are well-known security systems that can be used anywhere for authentication, authorization or any kind of security verifications. In biometric systems, the samples are trained first and then it can be used for testing in long runs. Many recent researches have shown that a biometric system may fail or get compromised because of the aging of the biometric templates. The fact that temporal duration affects the performance of the biometric system has shattered the belief that iris does not change over lifetime. This is also possible in the case of iris. So, the main focus of this work is to analyze the effect of aging and also to propose a new system that can deal with template aging. We have proposed a new iris recognition system with an image enhancement mechanism and different feature extraction mechanisms. In this work, three different features are extracted, which are then fused to be used as one. The full system is trained on a dataset of 2500 samples for the year 2008 and testing is done in three different phases (i) No-Lapse, (ii) 1-Year Lapse and (iii) 2-Year Lapse. A portion of the ND-Iris-Template-Aging dataset [11] is used with a period of three years lapse. Results show that the performance of the hybrid classifier AHyBrK [17] is improved as compared to KNN and ANN and the effect of aging in terms of degraded performance is clear. The performance of this system is measured in terms of False Rejection Rate, Error Rate, and Accuracy. The overall performance of AHyBrK is 51.04% and 52.98% better than KNN and ANN respectively in terms of False Rejection Rate and Error Rate whereas the accuracy of thisproposed system is also improved by 5.52% and 6.04% as compared to KNN and ANN respectively. This proposed system also achieved high accuracy for all the test phases.

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“…Due to successful spoofing attacks targeted at commercial iris sensors, numerous liveness detection methods were proposed at the sensor and software levels [13]. Temporal stability of iris patterns seems to be lower than initially believed [18,19,20,12,36] and hard to be estimated as we may observe even contradictory conclusions drawn from the same datasets [10,21]. New research in post-mortem iris recognition [42,41] reveals that it is possible to use this biometric characteristic in forensic applications.…”

Section: Iris Recognitionmentioning

confidence: 96%

Iris Recognition in Cases of Eye Pathology

Trokielewicz

Czajka

Maciejewicz

2018

Biometrics Under Biomedical Considerations

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This chapter provides insight on how iris recognition, one of the leading biometric identification technologies in the world, can be impacted by pathologies and illnesses present in the eye, what are the possible repercussions of this influence, and what are the possible means for taking such effects into account when matching iris samples.To make this study possible, a special database of iris images has been used, representing more than 20 different medical conditions of the ocular region (including cataract, glaucoma, rubeosis iridis, synechiae, iris defects, corneal pathologies and other) and containing almost 3000 samples collected from 230 distinct irises. Then, with the use of four different iris recognition methods, a series of experiments has been conducted, concluding in several important observations.One of the most popular ocular disorders worldwide -the cataract -is shown to worsen genuine comparison scores when results obtained from cataract-affected eyes are compared to those coming from healthy irises. An analysis devoted to different types of impact on eye structures caused by diseases is also carried out with significant results. The enrollment process is highly sensitive to those eye conditions that make the iris obstructed or introduce geometrical distortions. Disorders affecting iris geometry, or producing obstructions are exceptionally capable of degrading the genuine comparison scores, so that the performance of the entire biometric system can be influenced. Experiments also reveal that imperfect execution of the image segmentation stage is the most prominent contributor to recognition errors.

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Template Aging Phenomenon in Iris Recognition

Cited by 26 publications

References 30 publications

Lessons from collecting a million biometric samples

Lessons from collecting a million biometric samples

EAgeBioS: Enhanced Biometric System to handle the Effects of Template Aging

Iris Recognition in Cases of Eye Pathology

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