Limbus impact on off-angle iris degradation

Karakaya, Mahmut; Barstow, Del; Santos-Villalobos, Hector; Thompson, Joseph

doi:10.1109/icb.2013.6612971

Cited by 19 publications

(12 citation statements)

References 9 publications

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“…The shape of iris changes from being circle to ellipse. In addition to change in shape, image acquisition angle variation causes corneal distortions, depth-of-field out of focus blurring, limbus occlusions, and 3D iris texture distortions [3,6,10,12]. In order to design an accurate off-angle iris recognition system, all challenging aspects of off-angle irises must be addressed simultaneously.…”

Section: Effect Of Gaze Angle In Iris Recognitionmentioning

confidence: 99%

“…They reported that the limbus occlusion and other challenging issues in off-angle iris recognition are the main obstacles to reach a complete solution for real images. Karakaya et al [6] investigated the "limbus effect" by using real images and simulated experiments with real iris texture. They showed that the limbus effect degrades the performance of the off-angle iris recognition by increasing the hamming distance score between frontal and off-angle iris images ranging from 0.05 to 0.2 depending upon the limbus height.…”

Section: Introductionmentioning

confidence: 99%

“…The identification process in standoff systems begin with not only static individual standing in front of imaging device with perpendicular angle to his/her iris but also moving individuals with having off-angle iris images to imaging device. Therefore, captured iris images in standoff systems are negatively affected by several factors including image acquisition angle [3], occlusion [4], pupil dilation [5], limbus impact [6], image quality, focus, blur, specular reflections, and illumination variations [7,8]. Off-angle images causes the decrement in accuracy of iris recognition not only in standoff systems but also in constrained systems.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Effect Of Gaze Angle In Iris Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A gallery approach for off-angle iris recognition

2015

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“…Although it proved that corneal refraction elimination is possible at synthetic off-angle images, its correction at real off-angle iris images is limited due to other challenges in off-angle images such as limbus occlusion. Karakaya et al 20 reported the "limbus effect" on off-angle iris images in which the semitransparent limbus occludes iris texture at the sclera-iris boundaries and causes segmentation differences between frontal and off-angle images. Another study on off-angle iris images 21 showed that lens accommodation affects iris surface curvature and results in a performance drop.…”

Section: Related Workmentioning

confidence: 99%

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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“…Although their methods also showed some improvements with synthetic off-angle images, they noted that the main obstacle is a lack of a complete solution for real offangle iris images. Karakaya et al 13 investigated the limbus effect on off-angle iris images and showed that the limbus effect degrades the accuracy of stand-off iris recognition significantly.…”

Section: Related Work For Standoff Iris Recognitionmentioning

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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Limbus impact on off-angle iris degradation

Cited by 19 publications

References 9 publications

A gallery approach for off-angle iris recognition

A gallery approach for off-angle iris recognition

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

Effect of pupil dilation on off-angle iris recognition

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