An iris segmentation algorithm based on edge orientation for off-angle iris recognition

Karakaya, Mahmut; Barstow, Del; Santos-Villalobos, Hector; Boehnen, Christopher

doi:10.1117/12.2004444

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…To generate masked and normalized images, iris and pupil boundaries were segmented as ellipses ðC x ; C y ; R x ; R y ; θÞ using an off-angle iris segmentation algorithm. 48 The segmentation results for the iris-pupil (inner) boundary are almost perfect for all images except a few cases. However, due to changes in eyelid occlusions and gaze angle differences, iris-sclera (outer) boundary results are not consistent at the frontal and off-angle images.…”

Section: Dataset Preparationmentioning

confidence: 85%

Iris-ocular-periocular: toward more accurate biometrics for off-angle images

Karakaya

2021

J. Electron. Imag.

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Iris is one of the most well-known biometrics; it is a nonintrusive and contactless authentication technique with high accuracy, enhanced security, and unique distinctiveness. However, its dependence on image quality and its frontal image acquisition requirement limit its recognition performance and hinder its potential use in standoff applications. Standoff biometric systems require a less controlled environment than traditional systems, so their captured images will likely be nonideal, including off-angle. We present convolutional neural network (CNN)-based deep learning frameworks to improve the recognition performance of iris, ocular, and periocular biometric modalities for off-angle images. Our contribution is fourfold: first, the performances of popular AlexNet, GoogLeNet, and ResNet50 architectures are presented for off-angle biometrics. Second, we study the effect of the gaze angle difference between training and testing images on iris, ocular, and periocular recognitions. Third, we investigate the network behavior for untrained gaze angles and the information fusion capability of CNN networks at multiple off-angle images. Finally, deep learning-based results are compared with a traditional iris recognition algorithm using the gallery approach. Our results with off-angle images ranging from −50 deg to 50 deg in gaze angle show that the proposed methods improve the recognition performance of iris, ocular, and periocular recognition. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Dataset Preparationmentioning

confidence: 85%

Iris-ocular-periocular: toward more accurate biometrics for off-angle images

Karakaya

2021

J. Electron. Imag.

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“…After image acquisition, frontal and off-angle iris images are segmented using edge orientation based off-angle iris segmentation algorithm, 27 where pupil and iris boundaries are extracted as two ellipses. Since segmentation algorithm might introduce errors to Hamming distance calculations, we checked the segmentation results and fixed the errors using a manual ground-truth tool.…”

Section: Methodsmentioning

confidence: 99%

Effect of pupil dilation on off-angle iris recognition

Karakaya

Celik²

2019

J. Electron. Imag.

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Due to the less constrained setup in standoff iris recognition systems, it is likely to capture nonideal iris images with gaze angle, pupil dilation, reflections, and occlusions. The combined effect of pupil dilation and gaze angle on iris recognition is examined. We first highlight the effects on synthetic images generated with a biometric eye model using a ray-tracing algorithm. Then, we quantify the effects of pupil dilation and gaze angle on the real frontal and off-angle images at different dilation levels. Our experiments reveal that the larger differences in dilation levels and gaze angles between the compared iris images increase the Hamming distance. Even if the linear rubber-sheet normalization helps to minimize the dilation effect in frontal images, it cannot fully eliminate it in off-angle iris images because of not only the pupil dilation and three-dimensional iris texture but also the corneal refraction distortion and limbus occlusion. We also observe that the gaze angle is the main reason for the performance degradation in steeper off-angle images, where the effect of the dilation is limited. In addition, since the iris region in off-angle and dilated iris images is smaller than that in frontal and constricted iris images, their interclass Hamming distance distribution is shifted toward the intraclass distribution, which may increase the false match rate.

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“…As gender group, 46% of subjects are females and 56% of subjects are males. We have segmented the iris images by using the iris segmentation algorithm based on edge orientation for offangle iris recognition as described in [1]. To guarantee a valid segmentation and minimize the performance degradation because of the segmentation errors, we generate the ground-truth segmentation results for the ORNL-SOA dataset by using a tool in which at least two operators check the segmentation results to minimize the subjective decision of operators.…”

Section: Effect Of Gaze Angle In Iris Recognitionmentioning

confidence: 99%

“…So today, the use of biometric systems such as fingerprint, face, voice, signature, hand and vessel geometry, iris recognition are becoming increasingly common. Recent works on biometrics show that iris recognition is one of the most accurate, distinctive, and reliable biometric methods to verify the identity of an individual [1]. However, the accuracy of iris recognition systems depends on the quality of the data capture and similarity of the data capture conditions.…”

Section: Introductionmentioning

confidence: 99%

A gallery approach for off-angle iris recognition

2015

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It has been proven that hamming distance score between frontal and off-angle iris images of same eye differs in iris recognition system. The distinction of hamming distance score is caused by many factors such as image acquisition angle, occlusion, pupil dilation, and limbus effect. In this paper, we first study the effect of the angle variations between iris plane and the image acquisition systems. We present how hamming distance changes for different off-angle iris images even if they are coming from the same iris. We observe that increment in acquisition angle of compared iris images causes the increment in hamming distance. Second, we propose a new technique in off-angle iris recognition system that includes creating a gallery of different off-angle iris images (such as, 0, 10, 20, 30, 40, and 50 degrees) and comparing each probe image with these gallery images. We will show the accuracy of the gallery approach for off-angle iris recognition.

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An iris segmentation algorithm based on edge orientation for off-angle iris recognition

Cited by 8 publications

References 16 publications

Iris-ocular-periocular: toward more accurate biometrics for off-angle images

Iris-ocular-periocular: toward more accurate biometrics for off-angle images

Effect of pupil dilation on off-angle iris recognition

A gallery approach for off-angle iris recognition

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