Iris Liveness Detection Competition (LivDet-Iris) - The 2020 Edition

Das, Priyanka; Mcfiratht, Joseph; Fang, Zhaoyuan; Boyd, Aidan; Jang, Ganghee; Mohammadi, Amir; Purnapatra, Sandip; Yambay, David; Marcel, Sébastien; Trokielewicz, Mateusz; Maciejewicz, Piotr; Bowyer, Kevin W.; Czajka, Adam; Schuckers, Stephanie; Tapia, Juan; Gonzalez, Sebastian; Fang, Meiling; Damer, Naser; Kuijper, Arian; Sharma, Rajendra; Chen, Cunjian; Ross, Arun

doi:10.1109/ijcb48548.2020.9304941

Cited by 34 publications

(17 citation statements)

References 18 publications

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“…The reasons motivating our choice of DensetNet are: 1) whilst following a simple connectivity rule, DenseNets naturally integrate the properties of identity mappings and deep supervision. 2) DenseNet has already shown its superiority in ris PAD [31,24,5]. As shown in Fig.…”

Section: Baseline: Densenetmentioning

confidence: 93%

“…In recent years, iris recognition systems are being deployed in many law enforcement or civil applications [16,2,3]. However, iris recognition systems are vulnerable to Presentation Attacks (PAs) [32,5], performing to obfuscate the identity or impersonate a specific person ranging from printouts, replay, or textured contact lenses. Therefore, Presentation Attack Detection (PAD) field has received increasing attention to secure the recognition systems.…”

Section: Introductionmentioning

confidence: 99%

“…The LivDet-Iris is an international competition series launched in 2013 to assess the current state-of-the-art (SoTA) in the iris PAD field. The two most recent edition took place in 2017 [32] and 2020 [5]. The results reported in the LivDet-Iris 2017 [32] databases pointed out that there are still advancements to be made in the detection of iris PAs, especially under cross-PA, cross-sensor, or cross-database scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…The results reported in the LivDet-Iris 2017 [32] databases pointed out that there are still advancements to be made in the detection of iris PAs, especially under cross-PA, cross-sensor, or cross-database scenarios. Subsequently, LivDet-Iris 2020 [5] reported a significant performance degradation on novel PAs, showing that the iris PAD is still a challenging task. No specific training data was offered and the test data was not publicly available as of now for the LivDet-Iris 2020.…”

Section: Introductionmentioning

confidence: 99%

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Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Network

Fang¹,

Damer²,

Kirchbuchner³

et al. 2021

Preprint

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Iris presentation attack detection (PAD) plays a vital role in iris recognition systems. Most existing CNN-based iris PAD solutions 1) perform only binary label supervision during the training of CNNs, serving global information learning but weakening the capture of local discriminative features, 2) prefer the stacked deeper convolutions or expertdesigned networks, raising the risk of overfitting, 3) fuse multiple PAD systems or various types of features, increasing difficulty for deployment on mobile devices. Hence, we propose a novel attention-based deep pixel-wise binary supervision (A-PBS) method. Pixel-wise supervision is first able to capture the fine-grained pixel/patch-level cues. Then, the attention mechanism guides the network to automatically find regions that most contribute to an accurate PAD decision. Extensive experiments are performed on LivDet-Iris 2017 and three other publicly available databases to show the effectiveness and robustness of proposed A-PBS methods. For instance, the A-PBS model achieves an HTER of 6.50% on the IIITD-WVU database outperforming state-of-the-art methods.

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Section: Baseline: Densenetmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Network

Fang¹,

Damer²,

Kirchbuchner³

et al. 2021

Preprint

Self Cite

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“…Fast forward to today, fingerprint recognition systems are still being thwarted by spoof attacks with some success 9 . Across biometric traits, Apple's highly touted FaceID was compromised by a 3D mask shortly Iris 2020 LivDet-Iris Challenge 97.82% 1 1 Average accuracy reported in [95] and [96].…”

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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Iris Biometric Presentation Attack: Types and Detection Techniques—A Review

Singh

Rani

2022

Soft Computing: Theories and Applications

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Iris Liveness Detection Competition (LivDet-Iris) - The 2020 Edition

Cited by 34 publications

References 18 publications

Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Network

Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Network

Biometrics: Trust, but Verify

Iris Biometric Presentation Attack: Types and Detection Techniques—A Review

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