Visible Wavelength Iris Segmentation: A Multi-Class Approach using Fully Convolutional Neuronal Networks

Osorio-Roig, Daile; Rathgeb, Christian; Gomez‐Barrero, Marta; Morales-González, Annette; Garea-Llano, Eduardo; Busch, Christoph

doi:10.23919/biosig.2018.8553162

Cited by 12 publications

(14 citation statements)

References 16 publications

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“…For both databases segmentation groundtruth in terms of binary segmentation masks indicating iris/non-iris regions in each image are publicly available [22]. Images of both databases are processed using the iris segmentation method proposed in [10]. This algorithm has been shown to achieve state-of-the-art iris segmentation performance and is capable of performing a semantic segmentation of ocular images into several classes including iris, specular reflections and glasses, see section IV.…”

Section: Methodsmentioning

confidence: 99%

“…Firstly, the amount of specular reflections present in ocular images of subjects with and without glasses is estimated. For this purpose, the iris segmentation method proposed in [10], which automatically detects specular reflections, is used. The average fraction of pixels in images showing specular reflections is calculated for both databases.…”

Section: Impact On Iris Segmentationmentioning

confidence: 99%

“…More recently, methods based on deep learning, e.g. [7]- [10], revealed encouraging results for the task of iris segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Iris Recognition in Visible Wavelength: Impact and Automated Detection of Glasses

Roig

Drozdowski²,

Rathgeb

et al. 2018

2018 14th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

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The prevalence of visual impairment around the world is rapidly increasing, causing large numbers of people to wear glasses. Glasses are generally considered an important noise source in iris recognition; under objective metrics, they have recently been shown to deteriorate the sample quality of nearinfrared (NIR) ocular images (consequently impairing the segmentation accuracy and biometric performance). Automatically and robustly detecting glasses in ocular images is therefore one of the prerequisites for the acquisition of high quality iris samples. While this issue has recently been addressed for NIR ocular images, it remains an open issue in the visible wavelength (VW) spectrum. As the popularity of VW iris recognition increases (due to e.g. deployment of iris recognition in consumer grade mobile devices and general improvements in VW recognition algorithms), it becomes a matter of interest to quantitatively evaluate the impact of glasses on such systems, as well as develop methods for automatic detection of glasses in VW ocular images.In this paper, the impact of glasses on VW iris segmentation performance is investigated using the UBIRISv2 and MobBIO iris databases. It is shown that the presence of glasses significantly degrades the accuracy of iris segmentation. In addition, a state-ofthe-art iris segmentation method which can perform a semantic segmentation of ocular images (including the segmentation of glasses) is employed for the purpose of glasses detection. On the used databases, correct classification rates (CCRs) of 98.57% and 83.62% are obtained, respectively.

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Section: Methodsmentioning

confidence: 99%

Section: Impact On Iris Segmentationmentioning

confidence: 99%

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Iris Recognition in Visible Wavelength: Impact and Automated Detection of Glasses

Roig

Drozdowski²,

Rathgeb

et al. 2018

2018 14th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

Self Cite

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“…This dense block composites the feature values from the bottom layer to the top layer, which can effectively suppress overfitting and gradient vanishing. In contrast to the above methods, [89] does not change the network structure but sets the segmentation targets to multi-classes instead of just iris and non-iris regions. The training process of semantic segmentation is more complex than the segmentation based on two classes but captures more semantic information from the original iris image.…”

Section: Segmentation Based On Fully Conventional Neural Networkmentioning

confidence: 99%

Deep Learning for Iris Recognition: A Review

Yin¹,

He²,

Zhang³

et al. 2023

Preprint

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Iris recognition is a secure biometric technology known for its stability and privacy. With no two irises being identical and little change throughout a person's lifetime, iris recognition is considered more reliable and less susceptible to external factors than other biometric recognition methods. Unlike traditional machine learning-based iris recognition methods, deep learning technology does not rely on feature engineering and boasts excellent performance. This paper collects 120 relevant papers to summarize the development of iris recognition based on deep learning. We first introduce the background of iris recognition and the motivation and contribution of this survey. Then, we present the common datasets widely used in iris recognition. After that, we summarize the key tasks involved in the process of iris recognition based on deep learning technology, including identification, segmentation, presentation attack detection, and localization. Finally, we discuss the challenges and potential development of iris recognition. This review provides a comprehensive sight of the research of iris recognition based on deep learning.

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“…The work presented in [4] proposed a deep network called IrisDenseNet, which is based on VGG-16, to deal with low-quality iris images, such as side views, glasses, off-angle eye images and rotated eyes. Roig et al [38] proposed to segment the iris region using a multiclass approach, which differentiates additional classes, for example the pupil or sclera, aiming to improve the iris segmentation in non-cooperative environments using a CNN. Nevertheless, none of the above works provided a systematic analysis on the effect of the different gaze angles on ocular biometrics and the resulting iris segmentations and recognition using CNNs.…”

Section: Related Workmentioning

confidence: 99%

CNN‐based off‐angle iris segmentation and recognition

2021

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Accurate segmentation and parameterisation of the iris in eye images still remain a significant challenge for achieving robust iris recognition, especially in off-angle images captured in less constrained environments. While deep learning techniques (i.e. segmentation-based convolutional neural networks (CNNs)) are increasingly being used to address this problem, there is a significant lack of information about the mechanism of the related distortions affecting the performance of these networks and no comprehensive recognition framework is dedicated, in particular, to off-angle iris recognition using such modules. In this work, the general effect of different gaze angles on ocular biometrics is discussed, and the findings are then related to the CNN-based off-angle iris segmentation results and the subsequent recognition performance. An improvement scheme is also introduced to compensate for some segmentation degradations caused by the off-angle distortions, and a new gaze-angle estimation and parameterisation module is further proposed to estimate and re-project (correct) the offangle iris images back to frontal view. Taking benefit of these, several approaches (pipelines) are formulated to configure an end-to-end framework for the CNN-based offangle iris segmentation and recognition. Within the framework of these approaches, a series of experiments is carried out to determine whether (i) improving the segmentation outputs and/or correcting the output iris images before or after the segmentation can compensate for some off-angle distortions, (ii) a CNN trained on frontal eye images is capable of detecting and extracting the learnt features on the corrected images, or (iii) the generalisation capability of the network can be improved by training it on iris images of different gaze angles. Finally, the recognition performance of the selected approach is compared against some state-of-the-art off-angle iris recognition algorithms.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Visible Wavelength Iris Segmentation: A Multi-Class Approach using Fully Convolutional Neuronal Networks

Cited by 12 publications

References 16 publications

Iris Recognition in Visible Wavelength: Impact and Automated Detection of Glasses

Iris Recognition in Visible Wavelength: Impact and Automated Detection of Glasses

Deep Learning for Iris Recognition: A Review

CNN‐based off‐angle iris segmentation and recognition

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