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Proceedings ArticleDOI

Detecting medley of iris spoofing attacks using DESIST

TL;DR: 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.
Citations
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Proceedings ArticleDOI
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.
Abstract: Presentation attacks such as using a contact lens with a printed pattern or printouts of an iris can be utilized to bypass a biometric security system The first international iris liveness competition was launched in 2013 in order to assess the performance of presentation attack detection (PAD) algorithms, with a second competition in 2015 This paper presents results of the third competition, LivDet-Iris 2017 Three software-based approaches to Presentation Attack Detection were submitted Four datasets of live and spoof images were tested with an additional cross-sensor test New datasets and novel situations of data have resulted in this competition being of a higher difficulty than previous competitions Anonymous received the best results with a rate of rejected live samples of 336% and rate of accepted spoof samples of 1471% The results show that even with advances, printed iris attacks as well as patterned contacts lenses are still difficult for software-based systems to detect Printed iris images were easier to be differentiated from live images in comparison to patterned contact lenses as was also seen in previous competitions

92 citations

Journal ArticleDOI
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.
Abstract: Iris recognition is increasingly used in large-scale applications. As a result, presentation attack detection for iris recognition takes on fundamental importance. This survey covers the diverse research literature on this topic. 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. One conclusion from this is that presentation attack detection for iris recognition is not yet a solved problem. Datasets available for research are described, research directions for the near- and medium-term future are outlined, and a short list of recommended readings is suggested.

83 citations

Proceedings ArticleDOI
18 Jun 2018
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.
Abstract: Iris recognition systems may be vulnerable to presentation attacks such as textured contact lenses, print attacks, and synthetic iris images. Increasing applications of iris recognition have raised the importance of efficient presentation attack detection algorithms. In this paper, we propose a novel algorithm for detecting iris presentation attacks using a combination of handcrafted and deep learning based features. The proposed algorithm combines local and global Haralick texture features in multi-level Redundant Discrete Wavelet Transform domain with VGG features to encode the textural variations between real and attacked iris images. The proposed algorithm is extensively tested on a large iris dataset comprising more than 270,000 real and attacked iris images and yields a total error of 1.01%. The experimental evaluation demonstrates the superior presentation attack detection performance of the proposed algorithm as compared to state-of-the-art algorithms.

55 citations


Cites background or methods from "Detecting medley of iris spoofing a..."

  • ...It achieves 3 − 21% lower total error as compared to LBP, WLBP, and DESIST....

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  • ...It also achieves the lowest APCER value on LivDet2013, CSD, and LG4000 subset of NDCLD-2013....

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  • ...Comparative analysis is performed with existing PAD algorithms: LBP [6] , WLBP [10], DESIST [14], and the two components of MHVF algorithms: Multi-Level Haralick (MH) features and VGG features....

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  • ...[14] proposed an algorithm to detect a medley of iris presentation attacks and demonstrated the performance on a combined database of 21,525 iris images....

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  • ...Combined Spoofing Database [14] 21,525 Real, Print, Textured Contact Lens, and Synthetic Iris...

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Journal ArticleDOI
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).
Abstract: The adoption of large-scale iris recognition systems around the world has brought to light the importance of detecting presentation attack images (textured contact lenses and printouts). 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). Our method combines lightweight CNNs to classify multiple BSIF views of the input image. Following explorations on complementary input spaces leading to more discriminative features to detect presentation attacks, we also propose an algorithm to select the best (and most discriminative) predictors for the task at hand. An ensemble of predictors makes use of their expected individual performances to aggregate their results into a final prediction. Results show that this technique improves on the current state of the art in iris PAD, outperforming the winner of LivDet-Iris 2017 competition both for intra- and cross-dataset scenarios, and illustrating the very difficult nature of the cross-dataset scenario.

50 citations


Cites background from "Detecting medley of iris spoofing a..."

  • ...[61] contains images of live irises, textured contact lenses, iris printouts, and printouts of textured contact lenses....

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Proceedings ArticleDOI
18 Jun 2018
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.
Abstract: Iris recognition systems are vulnerable to presentation attacks where an adversary employs artifacts such as 2D prints of the eye, plastic eyes, and cosmetic contact lenses to obfuscate their own identity or to spoof the identity of another subject. In this work, we design a Convolutional Neural Network (CNN) architecture for presentation attack detection, that is observed to have good cross-dataset generalization capability. The salient features of the proposed approach include: (a) the use of the pre-normalized iris rather than the normalized iris, thereby avoiding spatial information loss; (b) the tessellation of the iris region into overlapping patches to enable data augmentation as well as to learn features that are location agnostic; (c) fusion of information across patches to enhance detection accuracy; (d) incorporating a "segmentation mask" in order to automatically learn the relative importance of the pupil and iris regions; (e) generation of a "heat map" that displays patch-wise presentation attack information, thereby accounting for artifacts that may impact only a small portion of the iris region. Experiments confirm the efficacy of the proposed approach.

49 citations


Cites background from "Detecting medley of iris spoofing a..."

  • ...Many researchers have investigated the use of local texture descriptors — such as LBP, LPQ, and BSIF — in conjunction with a Support Vector Machine or other classifiers [9, 31, 22, 11, 6, 5, 13]....

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References
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Journal ArticleDOI
TL;DR: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests.
Abstract: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests. The recognition principle is the failure of a test of statistical independence on iris phase structure encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 b/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. The high confidence levels are important because they allow very large databases to be searched exhaustively (one-to-many "identification mode") without making false matches, despite so many chances. Biometrics that lack this property can only survive one-to-one ("verification") or few comparisons. The paper explains the iris recognition algorithms and presents results of 9.1 million comparisons among eye images from trials in Britain, the USA, Japan, and Korea.

2,829 citations

Proceedings ArticleDOI
10 Dec 2002
TL;DR: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests.
Abstract: The principle that underlies the recognition of persons by their iris patterns is the failure of a test of statistical independence on texture phase structure as encoded by multiscale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 bits/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. Algorithms first described by the author in 1993 have now been tested in several independent field trials and are becoming widely licensed. This presentation reviews how the algorithms work and presents the results of 9.1 million comparisons among different eye images acquired in trials in Britain, the USA, Korea, and Japan.

2,437 citations


"Detecting medley of iris spoofing a..." refers methods in this paper

  • ...John Daugman patented the first successful iris recognition algorithm in 1994 [3]; it was based on a test of statistical independence of the phase of Gabor wavelets fitted on a grid of locations superimposed on a pseudo-polar transformation of the iris texture....

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Journal ArticleDOI
TL;DR: The experimental results on representative databases show that the proposed LBPV operator and global matching scheme can achieve significant improvement, sometimes more than 10% in terms of classification accuracy, over traditional locally rotation invariant LBP method.

782 citations


"Detecting medley of iris spoofing a..." refers methods in this paper

  • ...For this purpose, Local Binary Pattern Variance (LBPV) descriptor [6] is utilized....

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Journal ArticleDOI
TL;DR: This work assumes a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches based on convolutional networks.
Abstract: Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or spoofed) and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems, and attack types. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. The first approach consists of learning suitable convolutional network architectures for each domain, whereas the second approach focuses on learning the weights of the network via back propagation. We consider nine biometric spoofing benchmarks—each one containing real and fake samples of a given biometric modality and attack type—and learn deep representations for each benchmark by combining and contrasting the two learning approaches. This strategy not only provides better comprehension of how these approaches interplay, but also creates systems that exceed the best known results in eight out of the nine benchmarks. The results strongly indicate that spoofing detection systems based on convolutional networks can be robust to attacks already known and possibly adapted, with little effort, to image-based attacks that are yet to come.

353 citations


"Detecting medley of iris spoofing a..." refers background in this paper

  • ...In the literature, researchers have focused on one particular type of iris spoofing attack and have presented algorithms to address it [4, 10, 13]....

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Journal ArticleDOI
TL;DR: It is suggested that the performance from the Haar wavelet and Log-Gabor filter based phase encoding is the most promising among all the four approaches considered in this work and the combination of these two matchers is most promising, both in terms of performance and the computational complexity.

348 citations


"Detecting medley of iris spoofing a..." refers background in this paper

  • ...For IIITD IIS, SDB, IIT Delhi Iris, and MID, LUCID yields classification accuracy of 95.16%, 84.95%, 97.41%, and 84.96%, respectively....

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  • ...SDB [5] 1000 Synthetically Generated 2100 0 IIT Delhi Iris [9] 224 Normal 0 2240 MID 547 Normal 0 6022 CSD 1872 All Combined and Normal 11368 9325...

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  • ...• IIT Delhi Iris Database [9]: This database contains normal (non-spoofed) iris images of 224 subjects....

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  • ...For SDB, IIT Delhi Iris, and MID databases correct classification accuracy of 98.10%, 98.57%, and 88.55% is achieved by the DESIST framework....

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