Analysis of physical ageing effects in iris biometrics

Fairhurst, Michael; Erbilek, Meryem

doi:10.1049/iet-cvi.2010.0165

Cited by 36 publications

(44 citation statements)

References 20 publications

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“…Additionally, template effects were explored by Grother et al [17] who investigated the temporal stability of iris recognition systems. Their results confirm with the works of [18] and [15] that the difference in pupil dilation remains evident in iris recognition algorithms. Furthermore, Tomeo-Reyes and Chandran [19] performed bit error analysis to explore the consistence of texture information of the iris region from various degrees of dilation.…”

Section: B Related Work Within the Iris Recognition Communitysupporting

confidence: 89%

Understanding the subject-specific effects of pupil dilation on iris recognition in the NIR spectrum

Gejji¹,

Clark²,

Crihalmeanu³

et al. 2015

2015 IEEE International Symposium on Technologies for Homeland Security (HST)

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Abstract-A recent report from the National Institute of Standards and Technology (NIST) showed that changes in pupil dilation affect the performance of iris recognition algorithms. Hence, there is a need to explore the effects of pupil motion from a biological standpoint. Our work looks at the pupil's response to light, otherwise known as the pupil light reflex (PLR). By modeling the PLR using a nonlinear delay differential equation while considering images acquired in the near infrared (NIR) spectral band, we study both average and subject-specific pupil dilation effects. Experiments conducted on the WVU iris video dataset 1 convey the efficacy of our work in describing and evaluating pupillary response for both general and individual responses. The results of this work can be used to develop robust iris recognition algorithms that handle the effects of pupil dilation.

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Section: B Related Work Within the Iris Recognition Communitysupporting

confidence: 89%

Understanding the subject-specific effects of pupil dilation on iris recognition in the NIR spectrum

Gejji¹,

Clark²,

Crihalmeanu³

et al. 2015

2015 IEEE International Symposium on Technologies for Homeland Security (HST)

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“…We have ourselves also proposed a robust segmentation method [4] based on the [15] and the [12] segmentation algorithms. Masek firstly segments the iris, and then segments the pupil boundary within the detected iris region.…”

Section: Iris Segmentation Methodsmentioning

confidence: 99%

“…In this study, an experimental analysis is performed to test and compare the robustness, accuracy and computation time of the two earliest segmentation methods proposed by Daugman [2] and Wildes [3] with the more recently proposed methods introduced above [5][6][7][8][9][10][11][12]15] and with our proposed segmentation method [4].…”

Section: Experimental Infrastructurementioning

confidence: 99%

“…The merits of the iris modality in person recognition are well documented [1][2][3][4] but recognition performance is very dependent on the implementation of appropriate algorithms at all stages in the overall processing chain. If iris recognition is to be successfully applied in more general and less restricted environments, the capabilities of key processing stages will need to be developed accordingly.…”

Section: Introductionmentioning

confidence: 99%

“…We also include as an important component in our study a comparison with our own recently proposed segmentation method [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating iris segmentation for scenario optimisation

Erbilek¹,

Fairhurst²

2011

4th International Conference on Imaging for Crime Detection and Prevention 2011 (ICDP 2011)

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Iris recognition is a biometric modality which offers the potential for high accuracy and, increasingly, for application in more diverse environments than hitherto. Poor segmentation is one of the most important factors likely to compromise iris recognition performance. Hence, research in the area of iris biometrics has often been focused on efforts to enhance the performance of iris segmentation techniques, and this has led to considerable work on iris segmentation. This paper presents a detailed investigation, evaluation and comparison of several segmentation approaches (including a new algorithm proposed by the authors) proposed in the literature based on their accuracy and processing speed. To be consistent with the research of others, for all quantitative experiments, algorithms have been evaluated on two iris databases, namely CASIA V1.0 and a subset of the BioSecure database.

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Age Group Estimation from Human Iris

Rajput¹,

Sable²

2020

Advances in Intelligent Systems and Computing

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Analysis of physical ageing effects in iris biometrics

Cited by 36 publications

References 20 publications

Understanding the subject-specific effects of pupil dilation on iris recognition in the NIR spectrum

Understanding the subject-specific effects of pupil dilation on iris recognition in the NIR spectrum

Evaluating iris segmentation for scenario optimisation

Age Group Estimation from Human Iris

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