Unraveling the Effect of Textured Contact Lenses on Iris Recognition

Yadav, Daksha; Kohli, Naman; Doyle, James S.; Singh, Richa; Vatsa, Mayank; Bowyer, Kevin W.

doi:10.1109/tifs.2014.2313025

Cited by 149 publications

(103 citation statements)

References 15 publications

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“…the same cosmetic lens cannot be matched between different acquisition times (see Figure 1.2) [4,5,35,56]. However, soft contact lenses can be designed to maintain a specific orientation after eye blinking or any other movement by making it to be heavier at the bottom [5].…”

Section: Figure 12: Example Of Images Of Textured Lenses From the Ndmentioning

confidence: 99%

“…Textured contact lenses should be rejected before enrolment or during verification by labelling them as "failure to process", for instance [56]. The presence of cosmetic contact lenses is quite easy to reveal by manual visual inspection especially if the lens is not properly inserted or if the pure lens and original iris texture are otherwise separately observed [32].…”

Section: Figure 12: Example Of Images Of Textured Lenses From the Ndmentioning

confidence: 99%

“…In contrast, soft lenses, the most popular type of contacts, are much larger in diameter, thus it has been generally assumed for a long time that they do not substantially degrade the accuracy of iris recognition systems. However, the recent studies [4,5,35,56] have turned out this belief to be false.…”

Section: Figure 12: Example Of Images Of Textured Lenses From the Ndmentioning

confidence: 99%

“…Presence of both textured and transparent soft contact lenses is a another issue that may cause severe degradation in iris recognition performance in terms of false non-match rate (FNMR) [4,5,35,56]. The negative effect of contact lenses can be explained with general factors that can ob- '13) dataset [22] highlighting the variation in texture patterns between one genuine iris and three textured lens manufacturers, Cooper Vision, Johnson & Johnson and Ciba Vision, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Typically, the observed iris texture is a mixture of the lens and original iris texture because the synthetic texture band (1) is not fully opaque when the original genuine iris texture is partly visible through it, and (2) might not cover the whole iris region when a separate band of original iris texture is visible (see Figure 1.1) [8]. Still, it has been demonstrated that the presence of textured contact lenses yields to huge increase in FNMR when the gallery iris images of subjects with and without transparent contact lenses are compared with probe images of the same subjects with cosmetic lenses [4,5,35,56].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Contact lens detection in iris images

Komulainen¹,

Hadid²

2017

Iris and Periocular Biometric Recognition

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Iris texture provides the means for extremely accurate uni-modal person identification. However, the accuracy of iris based biometric systems is sensitive to the presence of contact lenses in acquired sample images. This is especially true in the case of textured (cosmetic) contact lenses that can be effectively used to obscure the original iris texture of a subject and consequently to perform presentation attacks. Since also transparent contact lenses can degrade matching rates, automatic detection and classification of different contact lens types is needed in order to improve the robustness of iris based biometric systems. This chapter introduces the problem of contact lens detection with particular focus on cosmetic contact lenses. The state of the art is analysed thoroughly and a case study on generalised textured contact lens detection is provided. The potential future research directions are also discussed.

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Section: Figure 12: Example Of Images Of Textured Lenses From the Ndmentioning

confidence: 99%

Section: Figure 12: Example Of Images Of Textured Lenses From the Ndmentioning

confidence: 99%

Section: Figure 12: Example Of Images Of Textured Lenses From the Ndmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Contact lens detection in iris images

Komulainen¹,

Hadid²

2017

Iris and Periocular Biometric Recognition

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Iris Liveness Detection by Modeling Dynamic Pupil Features

Czajka

2016

Handbook of Iris Recognition

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Deep Sclera Segmentation and Recognition

Rot

Vitek

Grm

et al. 2019

Handbook of Vascular Biometrics

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In this chapter, we address the problem of biometric identity recognition from the vasculature of the human sclera. Specifically, we focus on the challenging task of multi-view sclera recognition, where the visible part of the sclera vasculature changes from image to image due to varying gaze (or view) directions. We propose a complete solution for this task built around Convolutional Neural Networks (CNNs) and make several contributions that result in state-of-the-art recognition performance, i.e.: (i) we develop a cascaded CNN assembly that is able to robustly segment the sclera vasculature from the input images regardless of gaze direction, and (ii) we present ScleraNET, a CNN model trained in a multi-task manner (combining losses pertaining to identity and view-direction recognition) that allows for the extraction of discriminative vasculature descriptors that can be used for identity inference. To evaluate the proposed contributions, we also introduce a new dataset of ocular images, called the Sclera Blood Vessels, Periocular and Iris (SBVPI) dataset, which represents one of the few publicly available datasets suitable for research in multi-view sclera segmentation and recognition. The datasets come with a rich P. Rot and M. Vitek are first authors with equal contributions.

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Unraveling the Effect of Textured Contact Lenses on Iris Recognition

Cited by 149 publications

References 15 publications

Contact lens detection in iris images

Contact lens detection in iris images

Iris Liveness Detection by Modeling Dynamic Pupil Features

Deep Sclera Segmentation and Recognition

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