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
Citations
More filters
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
More filters
TL;DR: This paper presents a novel lens detection algorithm that can be used to reduce the effect of contact lenses and outperforms other lens detection algorithms on the two databases and shows improved iris recognition performance.
Abstract: The presence of a contact lens, particularly a textured cosmetic lens, poses a challenge to iris recognition as it obfuscates the natural iris patterns. The main contribution of this paper is to present an in-depth analysis of the effect of contact lenses on iris recognition. Two databases, namely, the IIIT-D Iris Contact Lens database and the ND-Contact Lens database, are prepared to analyze the variations caused due to contact lenses. We also present a novel lens detection algorithm that can be used to reduce the effect of contact lenses. The proposed approach outperforms other lens detection algorithms on the two databases and shows improved iris recognition performance.
149 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]....
[...]
01 Dec 2015
TL;DR: A novel and more accurate iris segmentation framework to automatically segment iris region from the face images acquired with relaxed imaging under visible or near-infrared illumination is proposed, which provides strong feasibility for applications in surveillance, forensics and the search for missing children, etc.
Abstract: This paper proposes a novel and more accurate iris segmentation framework to automatically segment iris region from the face images acquired with relaxed imaging under visible or near-infrared illumination, which provides strong feasibility for applications in surveillance, forensics and the search for missing children, etc. The proposed framework is built on a novel total-variation based formulation which uses l1 norm regularization to robustly suppress noisy texture pixels for the accurate iris localization. A series of novel and robust post processing operations are introduced to more accurately localize the limbic boundaries. Our experimental results on three publicly available databases, i.e., FRGC, UBIRIS.v2 and CASIA.v4-distance, achieve significant performance improvement in terms of iris segmentation accuracy over the state-of-the-art approaches in the literature. Besides, we have shown that using iris masks generated from the proposed approach helps to improve iris recognition performance as well. Unlike prior work, all the implementations in this paper are made publicly available to further advance research and applications in biometrics at-d-distance.
145 citations
"Unconstrained visible spectrum iris..." refers methods or result in this paper
...This is similar to the approach followed by Zhao and Kumar [22] for visible spectrum iris recognition....
[...]
...TV based segmentation algorithm [22] is proposed by Zhao and Kumar for segmenting unconstrained iris images including visible spectrum and they have demonstrated superior performance on databases such as UBIRIS.v2 [12] as compared to other approaches....
[...]
...1 [17], total variation (TV) based algorithm [22], and IrisSeg [2]....
[...]
...TV based segmentation algorithm [22] is proposed by Zhao and Kumar for segmenting unconstrained iris images including visible spectrum and they have demonstrated superior performance on databases such as UBIRIS....
[...]
23 Aug 2010
TL;DR: A novel fake iris detection algorithm based on improved LBP and statistical features is proposed, which achieves state-of-the-art performance in contact lens spoof detection.
Abstract: Spoof detection is a critical function for iris recognition because it reduces the risk of iris recognition systems being forged. Despite various counterfeit artifacts, cosmetic contact lens is one of the most common and difficult to detect. In this paper, we proposed a novel fake iris detection algorithm based on improved LBP and statistical features. Firstly, a simplified SIFT descriptor is extracted at each pixel of the image. Secondly, the SIFT descriptor is used to rank the LBP encoding sequence. Then, statistical features are extracted from the weighted LBP map. Lastly, SVM classifier is employed to classify the genuine and counterfeit iris images. Extensive experiments are conducted on a database containing more than 5000 fake iris images by wearing 70 kinds of contact lens, and captured by four iris devices. Experimental results show that the proposed method achieves state-of-the-art performance in contact lens spoof detection.
120 citations
"Unconstrained visible spectrum iris..." refers background or methods in this paper
...• Weighted LBP [21]: For classifying textured contact lens iris images in NIR spectrum, Zhang et al....
[...]
...Using DESIST, 85.00% of the real images are correctly classified as compared to 79.31% and 62.07% by multiscale BSIF and weighted LBP, respectively....
[...]
...• Weighted LBP [21]: For classifying textured contact lens iris images in NIR spectrum, Zhang et al. [21] have utilized Weighted Local Binary Patterns (Weighted LBP) in conjunction with SVM classifier....
[...]
...[21] have utilized Weighted Local Binary Patterns (Weighted LBP) in conjunction with SVM classifier....
[...]
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.
Abstract: Accurate iris recognition from the distantly acquired face or eye images requires development of effective strategies which can account for significant variations in the segmented iris image quality. Such variations can be highly correlated with the consistency of encoded iris features and the knowledge that such fragile bits can be exploited to improve matching accuracy. A non-linear approach to simultaneously account for both local consistency of iris bit and also the overall quality of the weight map is proposed. Our approach therefore more effectively penalizes the fragile bits while simultaneously rewarding more consistent bits. In order to achieve more stable characterization of local iris features, a Zernike moment-based phase encoding of iris features is proposed. Such Zernike moments-based phase features are computed from the partially overlapping regions to more effectively accommodate local pixel region variations in the normalized iris images. A joint strategy is adopted to simultaneously extract and combine both the global and localized iris features. The superiority of the proposed iris matching strategy is ascertained by providing comparison with several state-of-the-art iris matching algorithms on three publicly available databases: UBIRIS.v2, FRGC, CASIA.v4-distance. Our experimental results suggest that proposed strategy can achieve significant improvement in iris matching accuracy over those competing approaches in the literature, i.e., average improvement of 54.3%, 32.7% and 42.6% in equal error rates, respectively for UBIRIS.v2, FRGC, CASIA.v4-distance.
116 citations
"Unconstrained visible spectrum iris..." refers background in this paper
...Research in visible spectrum iris recognition [4, 10, 11, 18] has witnessed significant growth in recent years and is being actively explored....
[...]
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.
Abstract: Vulnerability of iris recognition systems remains a challenge due to diverse presentation attacks that fail to assure the reliability when adopting these systems in real-life scenarios. In this paper, we present 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. To this extent, we introduce a new relatively large scale visible spectrum iris artefact database comprised of 3300 iris normal and artefact samples that are captured by simulating five different attacks on iris recognition system. We also propose a novel presentation attack detection (PAD) scheme based on multiscale binarized statistical image features and linear support vector machines. Extensive experiments are carried out on four different publicly available iris artefact databases that have revealed the outstanding performance of the proposed PAD scheme when benchmarked with various well-established state-of-the-art schemes.
112 citations