Empirical Evaluation of Texture-Based Print and Contact Lens Iris Presentation Attack Detection Methods

Mandalapu, Hareesh; Ramachandra, Raghavendra; Busch, Christoph

doi:10.1145/3345336.3345341

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Comparison Of Open Source Methods2

Iris Presentation Attack Detection1

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2020

2021

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Cited by 3 publications

(3 citation statements)

References 24 publications

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“…Experiments also show that the fusion method [12] outperforms the other two methods in both known and unknown settings. This finding agrees with [23].…”

Section: Comparison Of Open Source Methodssupporting

confidence: 92%

See 1 more Smart Citation

Iris presentation attack detection: Where are we now?

Boyd

Fang

Czajka

et al. 2020

Pattern Recognition Letters

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“…Experiments also show that the fusion method [12] outperforms the other two methods in both known and unknown settings. This finding agrees with [23].…”

Section: Comparison Of Open Source Methodssupporting

confidence: 92%

“…In [23], the authors compared five different PAD methods on four different datasets whose PAIs include printouts and patterned contact lenses. All five methods are traditional vision-based methods, where the feature extractors are adopted from previous PAD papers.…”

Section: Comparison Of Methods Grouped By Datasetsmentioning

confidence: 99%

Iris presentation attack detection: Where are we now?

Boyd

Fang

Czajka

et al. 2020

Pattern Recognition Letters

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“…Various iris PAD frameworks are grouped into hand-crafted and deep-learningbased methods. Hand-crafted features such as Local Binary Pattern (LBP) [6], [19], [28], Binarized statistical image features (BSIF) as well as their variations have been the research focal points until 2015 and made a remarkable contribution to iris PAD problems. After that, with the rapid development and application of deep learning in multiple domains, especially, computer vision field, neural-network-based PAD methods showed up out of nowhere.…”

Section: Iris Presentation Attack Detectionmentioning

confidence: 99%

Demographic Bias in Presentation Attack Detection of Iris Recognition Systems

Fang

Damer

Kirchbuchner

et al. 2021

2020 28th European Signal Processing Conference (EUSIPCO)

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With the widespread use of biometric systems, the demographic bias problem raises more attention. Although many studies addressed bias issues in biometric verification, there is no works that analyse the bias in presentation attack detection (PAD) decisions. Hence, we investigate and analyze the demographic bias in iris PAD algorithms in this paper. To enable a clear discussion, we adapt the notions of differential performance and differential outcome to the PAD problem. We study the bias in iris PAD using three baselines (hand-crafted, transfer-learning, and training from scratch) using the the NDCLD-2013 [16] database. The experimental results points out that female users will be significantly less protected by the PAD, in comparison to males.

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