CIT-GAN: Cyclic Image Translation Generative Adversarial Network With Application in Iris Presentation Attack Detection

Yadav, Shivangi; Ross, Arun

doi:10.1109/wacv48630.2021.00246

Cited by 16 publications

(9 citation statements)

References 23 publications

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“…Generative methods have been used by some approaches, either to use the trained discriminator for iris PAD, or to generate synthetic samples and augment under-represented classes. In this direction, Yadav and Ross [193] proposed CIT-GAN (Cyclic Image Translation Generative Adversarial Network) for multi-domain style transfer to generate synthetic samples of several PAIs (cosmetic contact lenses, printed eyes, artificial eyes and kindle-display attack). To do so, image translation is driven by a Styling Network that learns style characteristics of each given domain.…”

Section: Adversarial Networkmentioning

confidence: 99%

“…Part of the problem lies into the limited size of existing databases, which is an issue for data-hungry DL approaches. Some solutions, as studied by some of the methods above, are data augmentation by geometric or illumination modifications [47], or creating additional synthetic data via generative methods [193]. Human-aided DL training is another promising avenue.…”

Section: Open Research Questions In Iris Padmentioning

confidence: 99%

“…However, in some environments such as mobile or distant capture, such sensing is not guaranteed [119]. Yadav and Ross [193] 2021 ✓ ✗ Casia-Iris-Fake, Berc-iris-fake, ND-CLD-15, LivDet-2017, MSU-IrisPA-01 BSIF, DESIST, VGG16, D-NetPAD, AlexNet…”

Section: Open Research Questions In Iris Padmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning for Iris Recognition: A Survey

Nguyen¹,

Proença²,

Alonso‐Fernandez³

2022

Preprint

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In this survey, we provide a comprehensive review of more than 200 papers, technical reports, and GitHub repositories published over the last 10 years on the recent developments of deep learning techniques for iris recognition, covering broad topics on algorithm designs, open-source tools, open challenges, and emerging research. First, we conduct a comprehensive analysis of deep learning techniques developed for two main sub-tasks in iris biometrics: segmentation and recognition. Second, we focus on deep learning techniques for the robustness of iris recognition systems against presentation attacks and via human-machine pairing. Third, we delve deep into deep learning techniques for forensic application, especially in post-mortem iris recognition. Fourth, we review open-source resources and tools in deep learning techniques for iris recognition. Finally, we highlight the technical challenges, emerging research trends, and outlook for the future of deep learning in iris recognition. CCS Concepts: • Security and privacy → Biometrics.

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Section: Adversarial Networkmentioning

confidence: 99%

Section: Open Research Questions In Iris Padmentioning

confidence: 99%

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Deep Learning for Iris Recognition: A Survey

Nguyen¹,

Proença²,

Alonso‐Fernandez³

2022

Preprint

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“…to capture features which differentiate bonafide acquisitions from PAs [101][102][103][104][105][106][107][108][109][110][111][112]. In contrast, software based solutions extract anatomical, physiological, textural, challenge response, or deep network based features to classify an input sample as live (bonafide) or presentation attack (spoof) [35,36,96,[98][99][100][113][114][115][116][117][118][119][120][121][122]. The culmination of these approaches can be seen in the high performances of the various algorithms submitted as part of the IARPA ODIN program 11 and also the public fingerprint and face liveness competitions (Table 2) [95,97].…”

Section: Presentation Attacksmentioning

confidence: 99%

Biometrics: Trust, but Verify

Jain¹,

Deb²,

Engelsma³

2021

Preprint

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Over the past two decades, biometric recognition has exploded into a plethora of different applications around the globe. This proliferation can be attributed to the high levels of authentication accuracy and user convenience that biometric recognition systems afford end-users. However, in-spite of the success of biometric recognition systems, there are a number of outstanding problems and concerns pertaining to the various sub-modules of biometric recognition systems that create an element of mistrust in their use -both by the scientific community and also the public at large. Some of these problems include: i) questions related to system recognition performance, ii) security (spoof attacks, adversarial attacks, template reconstruction attacks and demographic information leakage), iii) uncertainty over the bias and fairness of the systems to all users, iv) explainability of the seemingly black-box decisions made by most recognition systems, and v) concerns over data centralization and user privacy. In this paper, we provide an overview of each of the aforementioned open-ended challenges. We survey work that has been conducted to address each of these concerns and highlight the issues requiring further attention. Finally, we provide insights into how the biometric community can address core biometric recognition systems design issues to better instill trust, fairness, and security for all.

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“…Then the same group of researchers studied how to use those networks to detect more challenging attacks in [61]. Since those initial works based in deep learning, over the past few years, many works have continued to work in that direction, proposing novel PAD methods based on on deep architectures including attention learning [11,21], adversarial learning [69,70], and several approaches based on popular convolutional features [20].…”

Section: Software-based Approachesmentioning

confidence: 99%

Introduction to Presentation Attack Detection in Iris Biometrics and Recent Advances

Morales¹,

Fiérrez²,

Galbally³

et al. 2021

Preprint

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Iris recognition technology has attracted an increasing interest in the last decades in which we have witnessed a migration from research laboratories to real world applications. The deployment of this technology raises questions about the main vulnerabilities and security threats related to these systems. Among these threats presentation attacks stand out as some of the most relevant and studied. Presentation attacks can be defined as presentation of human characteristics or artifacts directly to the capture device of a biometric system trying to interfere its normal operation. In the case of the iris, these attacks include the use of real irises as well as artifacts with different level of sophistication such as photographs or videos. This chapter introduces iris Presentation Attack Detection (PAD) methods that have been developed to reduce the risk posed by presentation attacks. First, we summarise the most popular types of attacks including the main challenges to address. Secondly, we present a taxonomy of Presentation Attack Detection methods as a brief introduction to this very active research area. Finally, we discuss the integration of these methods into Iris Recognition Systems according to the most important scenarios of practical application.

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CIT-GAN: Cyclic Image Translation Generative Adversarial Network With Application in Iris Presentation Attack Detection

Cited by 16 publications

References 23 publications

Deep Learning for Iris Recognition: A Survey

Deep Learning for Iris Recognition: A Survey

Biometrics: Trust, but Verify

Introduction to Presentation Attack Detection in Iris Biometrics and Recent Advances

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