Posted Content•
Presentation Attack Detection for Iris Recognition: An Assessment of the State of the Art.
TL;DR: 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 are suggested.
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TL;DR: An overview of the existing publicly available datasets and their popularity in the research community using a bibliometric approach is provided to help investigators conducting research in the domain of iris recognition to identify relevant datasets.
Abstract: Research on human eye image processing and iris recognition has grown steadily over the last few decades. It is important for researchers interested in this discipline to know the relevant datasets in this area to (i) be able to compare their results and (ii) speed up their research using existing datasets rather than creating custom datasets. In this paper, we provide a comprehensive overview of the existing publicly available datasets and their popularity in the research community using a bibliometric approach. We reviewed 158 different iris datasets referenced from the 689 most relevant research articles indexed by the Web of Science online library. We categorized the datasets and described the properties important for performing relevant research. We provide an overview of the databases per category to help investigators conducting research in the domain of iris recognition to identify relevant datasets.
28 citations
Posted Content•
TL;DR: This work proposes an effective and robust iris PA detector called D-NetPAD based on the DenseNet convolutional neural network architecture that demonstrates generalizability across PA artifacts, sensors and datasets.
Abstract: An iris recognition system is vulnerable to presentation attacks, or PAs, where an adversary presents artifacts such as printed eyes, plastic eyes, or cosmetic contact lenses to circumvent the system. In this work, we propose an effective and robust iris PA detector called D-NetPAD based on the DenseNet convolutional neural network architecture. It demonstrates generalizability across PA artifacts, sensors and datasets. Experiments conducted on a proprietary dataset and a publicly available dataset (LivDet-2017) substantiate the effectiveness of the proposed method for iris PA detection. The proposed method results in a true detection rate of 98.58\% at a false detection rate of 0.2\% on the proprietary dataset and outperfoms state-of-the-art methods on the LivDet-2017 dataset. We visualize intermediate feature distributions and fixation heatmaps using t-SNE plots and Grad-CAM, respectively, in order to explain the performance of D-NetPAD. Further, we conduct a frequency analysis to explain the nature of features being extracted by the network. The source code and trained model are available at this https URL.
20 citations
Cites background from "Presentation Attack Detection for I..."
...Examples of PAIs in the case of the iris modality include printed iris images [7, 4, 27, 14], artificial eyes (plastic, glass, or doll eyes) [14, 22], cosmetic contacts [28, 37, 18], video display of an eye image [30, 6], ca-...
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...00 c ©2020 IEEE daver eyes [23, 6], robotic eye models [21] and holographic eye images [25]....
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...Czajka and Bowyer [6] also present a detailed assessment of various state-ofthe-art iris PA detection (PAD) algorithms....
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Posted Content•
TL;DR: This article proposes a method that combines two-dimensional and three-dimensional properties of the observed iris to address the problem of spoof detection in case when some properties of artifacts are unknown.
Abstract: Diversity and unpredictability of artifacts potentially presented to an iris sensor calls for presentation attack detection methods that are agnostic to specificity of presentation attack instruments. This paper proposes a method that combines two-dimensional and three-dimensional properties of the observed iris to address the problem of spoof detection in case when some properties of artifacts are unknown. The 2D (textural) iris features are extracted by a state-of-the-art method employing Binary Statistical Image Features (BSIF) and an ensemble of classifiers is used to deliver 2D modality-related decision. The 3D (shape) iris features are reconstructed by a photometric stereo method from only two images captured under near-infrared illumination placed at two different angles, as in many current commercial iris recognition sensors. The map of normal vectors is used to assess the convexity of the observed iris surface. The combination of these two approaches has been applied to detect whether a subject is wearing a textured contact lens to disguise their identity. Extensive experiments with NDCLD'15 dataset, and a newly collected NDIris3D dataset show that the proposed method is highly robust under various open-set testing scenarios, and that it outperforms all available open-source iris PAD methods tested in identical scenarios. The source code and the newly prepared benchmark are made available along with this paper.
19 citations
Cites background from "Presentation Attack Detection for I..."
...Since then iris PAD has become an increasingly popular research area, and a recent survey [1] provides a comprehensive study of the research up to date in iris PAD....
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...In iris recognition, possible forms of presentation attacks include using textured contact lenses, paper iris printouts, prosthetic eyes, or even cadaver eyes, since post-mortem iris recognition has been recently demonstrated to be possible [1]....
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TL;DR: A hybrid image encryption technique is proposed to secure multimedia data communication over 6G networks and achieves remarkably good performance as compared to the existing encryption techniques.
Abstract: With the rapid advancements of the internet of things (IoT), several applications have evolved with completely dissimilar structures and requirements. However, the fifth generation of mobile cellular networks (5G) is unable to successfully support the dissimilar structures and requirements. The sixth generation of mobile cellular networks (6G) is likely to enable new and unidentified applications with varying requirements. Therefore, 6G not only provides 10 to 100 times the speed of 5G, but 6G can also provide dynamic services for advanced IoT applications. However, providing security to 6G networks is still a significant problem. Therefore, in this paper, a hybrid image encryption technique is proposed to secure multimedia data communication over 6G networks. Initially, multimedia data are encrypted by using the proposed model. Thereafter, the encrypted data are then transferred over the 6G networks. Extensive experiments are conducted by using various attacks and security measures. A comparative analysis reveals that the proposed model achieves remarkably good performance as compared to the existing encryption techniques.
19 citations
Posted Content•
TL;DR: This paper presents results from the fourth competition of the LivDet-Iris series, and serves as the latest evaluation of iris PAD on a large spectrum of presentation attack instruments.
Abstract: Launched in 2013, LivDet-Iris is an international competition series open to academia and industry with the aim to assess and report advances in iris Presentation Attack Detection (PAD). This paper presents results from the fourth competition of the series: LivDet-Iris 2020. This year's competition introduced several novel elements: (a) incorporated new types of attacks (samples displayed on a screen, cadaver eyes and prosthetic eyes), (b) initiated LivDet-Iris as an on-going effort, with a testing protocol available now to everyone via the Biometrics Evaluation and Testing (BEAT)(this https URL) open-source platform to facilitate reproducibility and benchmarking of new algorithms continuously, and (c) performance comparison of the submitted entries with three baseline methods (offered by the University of Notre Dame and Michigan State University), and three open-source iris PAD methods available in the public domain. The best performing entry to the competition reported a weighted average APCER of 59.10\% and a BPCER of 0.46\% over all five attack types. This paper serves as the latest evaluation of iris PAD on a large spectrum of presentation attack instruments.
15 citations
Cites background from "Presentation Attack Detection for I..."
...Iris PAD literature offers a wide spectrum of softwareand hardware-based solutions, and two recent survey papers [4, 7] provide a comprehensive overview of the current state of the art....
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...Hardware solutions often employ active measurements of physical (color, density of tissue, optical properties) or physiological (pupil dilation) characteristics of the eye [7]....
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References
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TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Abstract: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.
42,067 citations
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TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
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11,211 citations
TL;DR: This work presents a high-level categorization of the various vulnerabilities of a biometric system and discusses countermeasures that have been proposed to address these vulnerabilities.
Abstract: Biometric recognition offers a reliable solution to the problem of user authentication in identity management systems. With the widespread deployment of biometric systems in various applications, there are increasing concerns about the security and privacy of biometric technology. Public acceptance of biometrics technology will depend on the ability of system designers to demonstrate that these systems are robust, have low error rates, and are tamper proof. We present a high-level categorization of the various vulnerabilities of a biometric system and discuss countermeasures that have been proposed to address these vulnerabilities. In particular, we focus on biometric template security which is an important issue because, unlike passwords and tokens, compromised biometric templates cannot be revoked and reissued. Protecting the template is a challenging task due to intrauser variability in the acquired biometric traits. We present an overview of various biometric template protection schemes and discuss their advantages and limitations in terms of security, revocability, and impact on matching accuracy. A template protection scheme with provable security and acceptable recognition performance has thus far remained elusive. Development of such a scheme is crucial as biometric systems are beginning to proliferate into the core physical and information infrastructure of our society.
1,119 citations
Patent•
10 Oct 1992
TL;DR: In this article, the sign of the projection of many different parts of the iris onto these filters determines each bit in an iris code, and the similarity metric (Hamming distance) is computed from the XOR of any two iris codes.
Abstract: Image analysis algorithms find the iris in a live video image (10) of a person's face, and encode its texture into an 'iris code' (24). Iris texture is extracted from the image at multiple scales of analysis by a self-similar set of quadrature bandpass filters defined in a dimensionless polar coordinate system. The sign of the projection of many different parts of the iris onto these filters determines each bit in an iris code. Comparisons between codes are readily implemented by the Exclusive-OR (XOR) logical operation. Pattern recognition is achieved by combining signal processing methods with statistical decision theory, leading to a statistical test of independence based on a similarity metric (Hamming distance) (26) that is computed from the XOR of any two iris codes. This measure positively establishes, confirms, or disconfirms, the identity of any individual (28). It also generates an objective confidence level (30) associated with the identification decision.
988 citations
TL;DR: A comprehensive survey of biometric cryptosystems and cancelable biometrics is presented and state-of-the-art approaches are reviewed based on which an in-depth discussion and an outlook to future prospects are given.
Abstract: Form a privacy perspective most concerns against the common use of biometrics arise from the storage and misuse of biometric data. Biometric cryptosystems and cancelable biometrics represent emerging technologies of biometric template protection addressing these concerns and improving public confidence and acceptance of biometrics. In addition, biometric cryptosystems provide mechanisms for biometric-dependent key-release. In the last years a significant amount of approaches to both technologies have been published. A comprehensive survey of biometric cryptosystems and cancelable biometrics is presented. State-of-the-art approaches are reviewed based on which an in-depth discussion and an outlook to future prospects are given.
620 citations