Unconstrained visible spectrum iris with textured contact lens variations: Database and benchmarking
TL;DR: The first contact lens database in visible spectrum, Unconstrained Visible Contact Lens Iris (UVCLI) Database, is introduced, containing samples from 70 classes with subjects wearing textured contact lenses in indoor and outdoor environments across multiple sessions and shows that there is a significant scope of improvement in developing efficient PAD algorithms for detection of texturedContact lenses in unconstrained visible spectrum iris images.
Abstract: Iris recognition in visible spectrum has developed into an active area of research This has elevated the importance of efficient presentation attack detection algorithms, particularly in security based critical applications In this paper, we present the first detailed analysis of the effect of textured contact lenses on iris recognition in visible spectrum We introduce the first contact lens database in visible spectrum, Unconstrained Visible Contact Lens Iris (UVCLI) Database, containing samples from 70 classes with subjects wearing textured contact lenses in indoor and outdoor environments across multiple sessions We observe that textured contact lenses degrade the visible spectrum iris recognition performance by over 25% and thus, may be utilized intentionally or unintentionally to attack existing iris recognition systems Next, three iris presentation attack detection (PAD) algorithms are evaluated on the proposed database and highest PAD accuracy of 8285%c is observed This illustrates that there is a significant scope of improvement in developing efficient PAD algorithms for detection of textured contact lenses in unconstrained visible spectrum iris images
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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
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: 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.
24 citations
01 Oct 2019
TL;DR: A general taxonomy of presentation attacks is proposed to cover different biometric modalities considering the attacker’s intention and the presentation instrument and mechanisms that aim to eliminate or mitigate those attacks are also taxonomized.
Abstract: Biometric-based recognition has been replacing conventional recognition methods in security systems. Modern electronic devices such as smartphones and online services have been employing biometric systems because of their security, acceptability, and usability. However, the wide deployment of Biometrics raises security concerns including attacks that aim to interfere with a system’s operation. This paper provides a review of potential threats which may affect biometric systems’ security, particularly, Presentation Attack (PA). A general taxonomy of presentation attacks is proposed to cover different biometric modalities considering the attacker’s intention and the presentation instrument. Moreover, Presentation Attack Detection (PAD) mechanisms that aim to eliminate or mitigate those attacks are also taxonomized. The taxonomy analyzes PAD mechanisms wherein the biometric trait pattern is considered to classify PAD methods. A state of the art study has been carried out to investigate PA and PAD for six biological and behavioral modalities.
14 citations
TL;DR: Deep Convolutional Neural Networks are used to detect spoofing techniques with superior results as compared to the existing state-of-the-art techniques on iris recognition.
Abstract: Iris recognition is used in various applications to identify a person. However, presentation attacks are making such systems vulnerable. Intruders can impersonate an individual to get entry into a system. In this paper, we have focused on print attacks, in which an intruder can use various techniques like printing of iris photographs to present to the sensor. Experiments conducted on the IIIT-WVU iris dataset show that print attack images of live iris images, use of contact lenses and conjunction of both can play a significant role in deceiving the iris recognition systems. The paper makes use of deep Convolutional Neural Networks to detect such spoofing techniques with superior results as compared to the existing state-of-the-art techniques.
9 citations
References
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24 Aug 2014
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.
Abstract: Human iris contains rich textural information which serves as the key information for biometric identifications. It is very unique and one of the most accurate biometric modalities. However, spoofing techniques can be used to obfuscate or impersonate identities and increase the risk of false acceptance or false rejection. This paper revisits iris recognition with spoofing attacks and analyzes their effect on the recognition performance. Specifically, print attack with contact lens variations is used as the spoofing mechanism. 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. The paper also presents the IIITD iris spoofing database, which contains over 4800 iris images pertaining to over 100 individuals with variations due to contact lens, sensor, and print attack. Finally, the paper also shows that cost effective descriptor approaches may help in counter-measuring spooking attacks.
105 citations
"Unconstrained visible spectrum iris..." refers background in this paper
...In the literature, researchers have demonstrated the impact of variety of presentation attacks on near-infrared (NIR) spectrum based iris recognition systems such as print/scan attacks [3], textured contact lens [5], and synthetic irises [16]....
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04 Jun 2013
TL;DR: An in-depth analysis of the effect of contact lens on iris recognition performance is presented and the results computed using VeriEye suggest that color cosmetic lens significantly increases the false rejection at a fixed false acceptance rate.
Abstract: Over the years, iris recognition has gained importance in the biometrics applications and is being used in several large scale nationwide projects. Though iris patterns are unique, they may be affected by external factors such as illumination, camera-eye angle, and sensor interoperability. The presence of contact lens, particularly color cosmetic lens, may also pose a challenge to iris biometrics as it obfuscates the iris patterns and changes the inter and intra-class distributions. This paper presents an in-depth analysis of the effect of contact lens on iris recognition performance. We also present the IIIT-D Contact Lens Iris database with over 6500 images pertaining to 101 subjects. For each subject, images are captured without lens, transparent (prescription) lens, and color cosmetic lens (textured) using two different iris sensors. The results computed using VeriEye suggest that color cosmetic lens significantly increases the false rejection at a fixed false acceptance rate. Also, the experiments on four existing lens detection algorithms suggest that incorporating lens detection helps in maintaining the iris recognition performance. However further research is required to build sophisticated lens detection algorithm that can improve iris recognition.
96 citations
"Unconstrained visible spectrum iris..." refers background in this paper
...In the literature, researchers have demonstrated the impact of variety of presentation attacks on near-infrared (NIR) spectrum based iris recognition systems such as print/scan attacks [3], textured contact lens [5], and synthetic irises [16]....
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01 Sep 2016
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.
77 citations
"Unconstrained visible spectrum iris..." refers background or methods in this paper
...The higher classification accuracy of second session indoor images can be associated with the fact that the DESIST algorithm is trained with images captured in indoor environment....
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...The combination of structural and textural features in DESIST is able to encode discriminatory information for iris presentation attack detection....
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...• DESIST [6]: DEtection of iriS spoofIng using Structural and Textural feature (DESIST) framework is proposed by Kohli et al....
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...DESIST is the state-of-the-art in detecting NIR based multiple iris presentation attacks....
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...4.2.1 Results of Real vs Attack Iris Detection Upon analyzing the results of experiment Real vs Attack Iris Detection in Table 3a and Figure 4a, we observe that DESIST framework [6] outperforms other approaches by at least 4% in distinguishing between visible spectrum real and attack (textured contact lens) images....
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01 Sep 2013
TL;DR: Experimental results in this work show that accuracy of textured lens detection can drop dramatically when tested on a manufacturer of lenses not seen in the training data, or when the iris sensor in use varies between the training and test data.
Abstract: Automatic detection of textured contact lenses in images acquired for iris recognition has been studied by several researchers. However, to date, the experimental results in this area have all been based on the same manufacturer of contact lenses being represented in both the training data and the test data and only one previous work has considered images from more than one iris sensor. Experimental results in this work show that accuracy of textured lens detection can drop dramatically when tested on a manufacturer of lenses not seen in the training data, or when the iris sensor in use varies between the training and test data. These results suggest that the development of a fully general approach to textured lens detection is a problem that still requires attention.
66 citations
"Unconstrained visible spectrum iris..." refers background in this paper
...Studies have also focused on developing algorithms for detection of contact lenses in iris images captured in NIR spectrum [1, 7, 20]....
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01 Oct 2006
TL;DR: Experiments confirm the potential of this scheme to generate a database of synthetic irises that can be used to evaluate the performance of iris recognition algorithms.
Abstract: We propose a technique to create digital renditions of iris images that can be used to evaluate the performance of iris recognition algorithms. The proposed scheme is implemented in two stages. In the first stage, a Markov random field model is used to generate a background texture representing the global iris appearance. In the next stage a variety of iris features, viz., radial and concentric furrows, collarette and crypts, are generated and embedded in the texture field. The iris images synthesized in this manner are observed to bear close resemblance to real irises. Experiments confirm the potential of this scheme to generate a database of synthetic irises that can be used to evaluate iris recognition algorithms.
62 citations
"Unconstrained visible spectrum iris..." refers background in this paper
...In the literature, researchers have demonstrated the impact of variety of presentation attacks on near-infrared (NIR) spectrum based iris recognition systems such as print/scan attacks [3], textured contact lens [5], and synthetic irises [16]....
[...]