Genetically identical irises have texture similarity that is not detected by iris biometrics

Hollingsworth, Karen; Bowyer, Kevin W.; Lagree, Stephen; Fenker, S. P.; Flynn, Patrick J.

doi:10.1016/j.cviu.2011.06.010

Cited by 29 publications

(16 citation statements)

References 17 publications

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“…It was estimated that the probability that two irises could produce exactly the same pattern is approximately 1 in 10 78 (as a reference, the population of the earth is around 10 10 ). Therefore, it has been widely accepted that no two irises are alike, even for the irises of twins, or for the left and right irises of the same individual [3]. Taking advantage of the clear distinctiveness of the iris patterns, automated iris biometrics systems based on Daugman's algorithms for encoding and recognizing [1] can distinguish individuals, even twin siblings [4].…”

Section: Introductionmentioning

confidence: 99%

“…Over the years, the communities have focused on the differences in iris textures for biometric identification applications. However, little research has addressed the question of whether genetically identical or related irises are sufficiently similar in some sense to correctly determine their relationship [3,4]. These similarities are not only interesting in fundamental research, but also useful in practical applications such as criminal investigation.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Genetically Identical Left and Right Irises Using a Convolutional Neural Network

Fang

Zhou

et al. 2019

Electronics

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As one of the most reliable biometric identification techniques, iris recognition has focused on the differences in iris textures without considering the similarities. In this work, we investigate the correlation between the left and right irises of an individual using a VGG16 convolutional neural network. Experimental results with two independent iris datasets show that a remarkably high classification accuracy of larger than 94% can be achieved when identifying if two irises (left and right) are from the same or different individuals. This exciting finding suggests that the similarities between genetically identical irises that are indistinguishable using traditional Daugman’s approaches can be detected by deep learning. We expect this work will shed light on further studies on the correlation between irises and/or other biometric identifiers of genetically identical or related individuals, which would find potential applications in criminal investigations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Genetically Identical Left and Right Irises Using a Convolutional Neural Network

Fang

Zhou

et al. 2019

Electronics

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“…In such a case, the LOO rule may not be accurate when classifying a person, who may be a twin, into his/her unique class (or identity) in a biometric person identification system due to the shared features. The literature on face biometric, for example, has looked into unique features to uniquely identify a person with twin [1][2][3][4], as have other researchers [5,6].…”

Section: Introductionmentioning

confidence: 99%

Matched-Pair Classification Analysis on Siblings' Gait Biometric Data

Mohd-Isa¹,

Abdullah²,

Eswaran³

et al. 2014

JCSCM

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This paper presents a supervised classification task on gait biometric of siblings' data sets. This task, which we refer to as matched-pair classification, evaluates the within pair differences in terms of the data set via jackknifing. A misclassification rate (MCR), which measures the percentage of misclassification of one sib compared to the other, gives an estimate on the potential uniqueness of gait for a person, particularly in twins. By this approach, the MCR values are mostly in the range of 90% for a data set of twins and in the range of 80% for a data set of non-twin siblings. When compared to the standard Leave-One-Out (LOO) classification, the MCR values of the proposed approach are higher than the LOO classification, which may suggest its potential use in machine learning with regard to biometric-based systems.

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“…Karen et al [1] states that, genetically identical irises have texture similarity that is not detected by iris biometrics. By performing experimental studies on left and right irises of the same person, it results that are as different as irises of unrelated people.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Iris Recognition System on Multiple Feature Extraction Algorithms and its Combinations

Ashwini¹,

Imran²,

Alsaade³

2015

IJCATR

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Multi-algorithmic approach to enhancing the accuracy of iris recognition system is proposed and investigated. In this system, features are extracted from the iris using various feature extraction algorithms, namely LPQ, LBP, Gabor Filter, Haar, Db8 and Db16. Based on the experimental results, it is demonstrated that Mutli-algorithms Iris Recognition System is performing better than the unimodal system. The accuracy improvement offered by the proposed approach also showed that using more than two feature extraction algorithms in extracting the iris system might decrease the system performance. This is due to redundant features. The paper presents a detailed description of the experiments and provides an analysis of the performance of the proposed method.

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Genetically identical irises have texture similarity that is not detected by iris biometrics

Cited by 29 publications

References 17 publications

Classification of Genetically Identical Left and Right Irises Using a Convolutional Neural Network

Classification of Genetically Identical Left and Right Irises Using a Convolutional Neural Network

Matched-Pair Classification Analysis on Siblings' Gait Biometric Data

Evaluation of Iris Recognition System on Multiple Feature Extraction Algorithms and its Combinations

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