Proceedings ArticleDOI
Detecting medley of iris spoofing attacks using DESIST
Naman Kohli,Daksha Yadav,Mayank Vatsa,Richa Singh,Afzel Noore +4 more
- pp 1-6
TLDR
A novel structural and textural feature based iris spoofing detection framework (DESIST) is proposed which combines multi-order dense Zernike moments and Local Binary Pattern with Variance for representing textural changes in a spoofed iris image.Abstract:
Human iris is considered a reliable and accurate modality for biometric recognition due to its unique texture information. However, similar to other biometric modalities, iris recognition systems are also vulnerable to presentation attacks (commonly called spoofing) that attempt to conceal or impersonate identity. Examples of typical iris spoofing attacks are printed iris images, textured contact lenses, and synthetic creation of iris images. It is critical to note that majority of the algorithms proposed in the literature are trained to handle a specific type of spoofing attack. These algorithms usually perform very well on that particular attack. However, in real-world applications, an attacker may perform different spoofing attacks. In such a case, the problem becomes more challenging due to inherent variations in different attacks. In this paper, we focus on a medley of iris spoofing attacks and present a unified framework for detecting such attacks. We propose a novel structural and textural feature based iris spoofing detection framework (DESIST). Multi-order dense Zernike moments are calculated across the iris image which encode variations in structure of the iris image. Local Binary Pattern with Variance (LBPV) is utilized for representing textural changes in a spoofed iris image. The highest classification accuracy of 82.20% is observed by the proposed framework for detecting normal and spoofed iris images on a combined iris spoofing database.read more
Citations
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Proceedings ArticleDOI
LivDet iris 2017 — Iris liveness detection competition 2017
David Yambay,Benedict Becker,Naman Kohli,Daksha Yadav,Adam Czajka,Kevin W. Bowyer,Stephanie Schuckers,Richa Singh,Mayank Vatsa,Afzel Noore,Diego Gragnaniello,Carlo Sansone,Luisa Verdoliva,Lingxiao He,Yiwei Ru,Haiqing Li,Nianfeng Liu,Zhenan Sun,Tieniu Tan +18 more
TL;DR: Results of the third LivDet-Iris 2017 show that even with advances, printed iris attacks as well as patterned contacts lenses are still difficult for software-based systems to detect.
Journal ArticleDOI
Presentation Attack Detection for Iris Recognition: An Assessment of the State-of-the-Art
Adam Czajka,Kevin W. Bowyer +1 more
TL;DR: In this paper, different categories of presentation attack are described and placed in an application-relevant framework, and the state-of-the-art in detecting each category of attack is summarized.
Proceedings ArticleDOI
Fusion of Handcrafted and Deep Learning Features for Large-Scale Multiple Iris Presentation Attack Detection
TL;DR: A novel algorithm for detecting iris presentation attacks using a combination of handcrafted and deep learning based features in multi-level Redundant Discrete Wavelet Transform domain with VGG features to encode the textural variations between real and attacked iris images.
Journal ArticleDOI
Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection
TL;DR: This paper presents a new approach in iris presentation attack detection (PAD) by exploring combinations of convolutional neural networks (CNNs) and transformed input spaces through binarized statistical image features (BSIFs).
Proceedings ArticleDOI
Convolutional Neural Networks for Iris Presentation Attack Detection: Toward Cross-Dataset and Cross-Sensor Generalization
TL;DR: A Convolutional Neural Network architecture for presentation attack detection, that is observed to have good cross-dataset generalization capability and to use the pre-normalized iris rather than the normalized iris, thereby avoiding spatial information loss.
References
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Journal ArticleDOI
Accurate Iris Recognition at a Distance Using Stabilized Iris Encoding and Zernike Moments Phase Features.
Chun-Wei Tan,Ajay Kumar +1 more
TL;DR: This work proposes a nonlinear approach to simultaneously account for both local consistency of iris bit and also the overall quality of the weight map, which more effectively penalizes the fragile bits while simultaneously rewarding more consistent bits.
Journal ArticleDOI
Iris Image Classification Based on Hierarchical Visual Codebook
TL;DR: Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification.
Journal ArticleDOI
Robust Scheme for Iris Presentation Attack Detection Using Multiscale Binarized Statistical Image Features
TL;DR: An in-depth analysis of presentation attacks on iris recognition systems especially focusing on the photo print attacks and the electronic display (or screen) attack is presented and a novel presentation attack detection (PAD) scheme based on multiscale binarized statistical image features and linear support vector machines is proposed.
Book ChapterDOI
Direct Attacks Using Fake Images in Iris Verification
Virginia Ruiz-Albacete,Pedro Tome-Gonzalez,Fernando Alonso-Fernandez,Javier Galbally,Julian Fierrez,Javier Ortega-Garcia +5 more
TL;DR: It is shown that the iris-based recognition system is vulnerable to direct attacks, pointing out the importance of having countermeasures against this type of fraudulent actions.
Proceedings ArticleDOI
On Iris Spoofing Using Print Attack
TL;DR: It is observed that print attack and contact lens, individually and in conjunction, can significantly change the inter-personal and intra-personal distributions and thereby increase the possibility to deceive the iris recognition systems.