Does Iris Change Over Time?

Mehrotra, Hunny; Vatsa, Mayank; Singh, Richa; Majhi, Banshidhar

doi:10.1371/journal.pone.0078333

Cited by 21 publications

(14 citation statements)

References 15 publications

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“…In Notre Dame's data there is, due to systematic dilation changes seen in Fig. 1 and likely caused by uncontrolled ambient covariates [4], primarily illumination [1]. As a result, their 'ageing' estimate 3.5 × 10 −5 day −1 is 40 times higher than ours and should be discounted in any discussion of iris permanence.…”

Section: Truncated Datacontrasting

confidence: 58%

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

2015

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Section: Truncated Datacontrasting

confidence: 58%

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

2015

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“…Eventually, testing different quality measures we can confirm claims in [13] that quality plays a critical role in accuracy. Even the age of the person may be an impacting quality attribute itself, so we tested this impact splitting up the dataset by its average value, 28.13 years.…”

Section: On the Impact Of Quality Predicates On Accuracymentioning

confidence: 63%

“…Given that the literature has no agreed view on the significance of ageing effects in iris recognition, it seems reasonable to further investigate reasons for observed deviations in results. Mehrothra et al [13] investigate ageing on ND-Iris-Template-Aging-2008-2010 and ND-TimeLapseIris-2012 databases, and find that the reported increased false rejection rate may apart from pupil dilation also be attributed to other factors, such as occlusion, blur, rotation, illumination, and noise. Moreover Ortiz et al [19] find, that obviously, a high difference in pupillary size and low absolute pupillary values create low HD scores.…”

Section: Iet Biometricsmentioning

confidence: 99%

Impact of (segmentation) quality on long vs. short‐timespan assessments in iris recognition performance

2015

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Several researchers have presented studies of temporal effects on iris recognition accuracy, with varying results on severity of observed effects. The sensitive topic continues to be adversely discussed and the difficulty of isolating performance-impacting factors is immanent. The impact of ageing on segmentation versus feature extraction has been largely neglected so far. This paper attempts to shed light on the impact of segmentation quality on observed temporal effects highlighting the critical role of the segmentation module and quality assessment when assessing ageing effects. The lack of large and standardized temporal variation in public datasets as well as additional metadata (age of subject, recording parameters) and strictly enforced unified recording and quality guidelines over time-separated sessions are identified as imminent problems of ageing studies. Results are reported on a long-timespan database of 36,240 images comprising 104 classes and a 4-year time lapse, offering a large variety of recording conditions highlighting the critical role of a transparent recording setup.

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“…To analyze the effect of aging and the other factors that change the iris template, Mehrotra et al [21] also did experiments with the "ND-Iris-TemplateAging-2008-2010" and ND-TimeLapseIris-2012" datasets which are publicly available. For matching, they used VeriEye [23] which is a commercial matcher.…”

Section: Effect Of Template Aging On Iris Based Biometric Systemsmentioning

confidence: 99%

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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Does Iris Change Over Time?

Cited by 21 publications

References 15 publications

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

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

Impact of (segmentation) quality on long vs. short‐timespan assessments in iris recognition performance

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

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