IREX VI: mixed‐effects longitudinal models for iris ageing: response to Bowyer and Ortiz

Grother, Patrick J.; Matey, James R.; Quinn, George

doi:10.1049/iet-bmt.2015.0043

Cited by 14 publications

(11 citation statements)

References 21 publications

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“…To address these concerns, NIST undertook an effort to better understand the effect of ageing and other factors on iris biometrics [2]. This effort opened a whole new range of questions related to the factors that affect iris recognition and the ways iris biometrics is evaluated [4][5][6][7][8][9].…”

Section: Discussionmentioning

confidence: 99%

“…These metrics are listed in Table 1. The metrics that were shared with NIST and UND and used in the previous research [2][3][4][5][6][7][8][9] are marked in bold.…”

Section: Ops-xing Datasetmentioning

confidence: 99%

“…The OPS‐XING dataset, a part of which was used in the IREX VI evaluation by NIST [2, 4] and the evaluations conducted by UND [5, 6, 9], consists of over a quarter billion of matching and IQ metrics that were recorded during enrolment and passage transactions by the NEXUS system. These metrics are listed in Table 1.…”

Section: Ops‐xing Datasetmentioning

confidence: 99%

“…In 2012, a portion of anonymised NEXUS kiosk log data was shared with the National Institute of Standard and Technology (NIST) scientists for the IREX VI study aimed at investigating the effect of aging on iris performance [2], where it was labelled the OPS-XING dataset. The results of IREX VI study, which were published in 2013, brought a lot of attention from the scientific community, actively discussed and contested by the scientists from the University of Notre Dame [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Analysis of the effect of ageing, age, and other factors on iris recognition performance using NEXUS scores dataset

Gorodnichy¹,

Chumakov²

2018

IET biom.

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The historical NEXUS iris kiosks log dataset collected by the Canada Border Services Agency from 2003 to 2014 has become the focus of scientific attention due to its involvement in the iris ageing debate between the National Institute of Standard and Technology and the University of Notre Dame researchers. To facilitate this debate, this study provides additional details on how this dataset was collected, its various properties and irregularities, and presents new results related to the effect of ageing, age, and other factors on the system performance obtained using the portions of the dataset that have not been previously analysed. In doing that, the importance of conducting subject-based performance analysis, as opposed to the traditionally done transaction-based analysis, is emphasised. The significance of factor effects is examined. Recommendations on further improvement of the technology are made.

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Section: Discussionmentioning

confidence: 99%

“…These metrics are listed in Table 1. The metrics that were shared with NIST and UND and used in the previous research [2][3][4][5][6][7][8][9] are marked in bold.…”

Section: Ops-xing Datasetmentioning

confidence: 99%

Section: Ops‐xing Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of the effect of ageing, age, and other factors on iris recognition performance using NEXUS scores dataset

Gorodnichy¹,

Chumakov²

2018

IET biom.

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“…• IREX VI [17,18] explored a possible aging effect for iris recognition. The intrinsic features of the iris may naturally change over time in a way that affects recognition accuracy.…”

Section: The Irex Programmentioning

confidence: 99%

IREX IX part two:

Quinn¹,

Grother²,

Matey³

2019

Self Cite

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Specific hardware and software products identified in this report were used in order to perform the evaluations described in this document. In no case does identification of any commercial product, trade name, or vendor, imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the products and equipment identified are necessarily the best available for the purpose. Caution is advised when attempting to extrapolate numerical results from this evaluation to arbitrary applications. This evaluation measures performance over a particular test dataset collected under specific environmental conditions with specific hardware. It is difficult to predict how changing any of these parameters might affect performance. Institutional Review Board The National Institute of Standards and Technology Human Subjects Protection Office (HSPO) reviewed the protocol for this project and determined it is not human subjects research as defined in Department of Commerce Regulations, 15 CFR 27, also known as the Common Rule for the Protection of Human Subjects (45 CFR 46, Subpart A).

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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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IREX VI: mixed‐effects longitudinal models for iris ageing: response to Bowyer and Ortiz

Cited by 14 publications

References 21 publications

Analysis of the effect of ageing, age, and other factors on iris recognition performance using NEXUS scores dataset

Analysis of the effect of ageing, age, and other factors on iris recognition performance using NEXUS scores dataset

IREX IX part two:

Iris Recognition in Cases of Eye Pathology

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