Revisiting iris recognition with color cosmetic contact lenses

doi:10.1109/ICB.2013.6613021

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Revisiting iris recognition with color cosmetic contact lenses

Proceedings Article•DOI•

Revisiting iris recognition with color cosmetic contact lenses

Naman Kohli¹, Daksha Yadav¹, Mayank Vatsa¹, Richa Singh¹•Institutions (1)

Indraprastha Institute of Information Technology¹

04 Jun 2013-pp 1-7

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.

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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.

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Journal Article•DOI•

Deep Representations for Iris, Face, and Fingerprint Spoofing Detection

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David Menotti¹, Giovani Chiachia¹, Allan Pinto¹, William Robson Schwartz², Helio Pedrini¹, Alexandre X. Falcão¹, Anderson Rocha¹ - Show less +3 more•Institutions (2)

State University of Campinas¹, Universidade Federal de Minas Gerais²

02 Feb 2015-IEEE Transactions on Information Forensics and Security

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.

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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.

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353 citations

Cites methods from "Revisiting iris recognition with co..."

...Both works validated the methods with the Print Attack benchmark [53]....
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Journal Article•DOI•

Deep Representations for Iris, Face, and Fingerprint Spoofing Detection

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David Menotti¹, Giovani Chiachia¹, Allan Pinto¹, William Robson Schwartz², Helio Pedrini¹, Alexandre X. Falcão¹, Anderson Rocha¹ - Show less +3 more•Institutions (2)

State University of Campinas¹, Universidade Federal de Minas Gerais²

08 Oct 2014-arXiv: Computer Vision and Pattern Recognition

TL;DR: In this paper, the authors proposed two deep learning approaches for spoofing detection of iris, face, and fingerprint modalities based on a very limited knowledge about biometric spoofing at the sensor.

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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, while 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.

...read moreread less

325 citations

Journal Article•DOI•

Unraveling the Effect of Textured Contact Lenses on Iris Recognition

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Daksha Yadav¹, Naman Kohli¹, James S. Doyle², Richa Singh³, Mayank Vatsa³, Kevin W. Bowyer² - Show less +2 more•Institutions (3)

West Virginia University¹, University of Notre Dame², Indraprastha Institute of Information Technology³

01 May 2014-IEEE Transactions on Information Forensics and Security

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.

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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.

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149 citations

Cites background from "Revisiting iris recognition with co..."

...Though iris features are unique, recent research results suggest that they are affected by several covariates such as pupil dilation [3] and sensor interoperability [4], [5]....
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...Sometimes, lenses also have a noticeable boundary between the support region of the lens and the corrective region of the lens, which can also alter the appearance of the iris texture....
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...Table I...
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...Dataset III is simply the union of Dataset I and Dataset II resulting in a multi-camera training set of 3600 images and a verification set of 1500 images....
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...Classes (1) and (2) are balanced between male and female, and represent a variety of ethnicities....
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Journal Article•DOI•

Detecting Silicone Mask-Based Presentation Attack via Deep Dictionary Learning

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Ishan Manjani¹, Snigdha Tariyal¹, Mayank Vatsa¹, Richa Singh¹, Angshul Majumdar¹ - Show less +1 more•Institutions (1)

Indraprastha Institute of Information Technology¹

01 Jul 2017-IEEE Transactions on Information Forensics and Security

TL;DR: This paper introduces the first-of-its-kind silicone mask attack database which contains 130 real and attacked videos to facilitate research in developing presentation attack detection algorithms for this challenging scenario.

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Abstract: In movies, film stars portray another identity or obfuscate their identity with the help of silicone/latex masks. Such realistic masks are now easily available and are used for entertainment purposes. However, their usage in criminal activities to deceive law enforcement and automatic face recognition systems is also plausible. Therefore, it is important to guard biometrics systems against such realistic presentation attacks. This paper introduces the first-of-its-kind silicone mask attack database which contains 130 real and attacked videos to facilitate research in developing presentation attack detection algorithms for this challenging scenario. Along with silicone mask, there are several other presentation attack instruments that are explored in literature. The next contribution of this research is a novel multilevel deep dictionary learning-based presentation attack detection algorithm that can discern different kinds of attacks. An efficient greedy layer by layer training approach is formulated to learn the deep dictionaries followed by SVM to classify an input sample as genuine or attacked. Experimental are performed on the proposed SMAD database, some samples with real world silicone mask attacks, and four existing presentation attack databases, namely, replay-attack, CASIA-FASD, 3DMAD, and UVAD. The results show that the proposed algorithm yields better performance compared with state-of-the-art algorithms, in both intra-database and cross-database experiments.

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145 citations

Cites background from "Revisiting iris recognition with co..."

...attack detection in other biometric modalities such as detecting contact lens and print attack in iris [59]–[61]....
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Journal Article•DOI•

Ocular biometrics

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Ishan Nigam¹, Mayank Vatsa¹, Richa Singh¹•Institutions (1)

Indraprastha Institute of Information Technology¹

01 Nov 2015-Information Fusion

TL;DR: A path forward is proposed to advance the research on ocular recognition by improving the sensing technology, heterogeneous recognition for addressing interoperability, utilizing advanced machine learning algorithms for better representation and classification, and developing algorithms for ocular Recognition at a distance.

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138 citations

Cites methods from "Revisiting iris recognition with co..."

...[128] investigate the role of contact lenses in obfuscation of iris texture and analyze the effect of contact lenses on the performance of iris recognition using the IIITD CLI database, which consists of images captured without lens, with transparent (prescription) lens, and with color cosmetic lens....
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...The pre-consumption and post-consumption overlap between genuine and impostor match scores increases by approximately 20% Yadav et al. [129] IIITD CLI [128], ND-Contact Lens [147] Investigate the role of contact lenses in obfuscation of iris texture and analyze the effect of contact lenses on the performance of iris recognition Gupta et al. [130] IIITD Iris Spoofing [130] Investigate iris recognition with respect to print spoofing attacks....
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...Kohli et al. [128] investigate the role of contact lenses in obfuscation of iris texture and analyze the effect of contact lenses on the performance of iris recognition using the IIITD CLI database, which consists of images captured without lens, with transparent (prescription) lens, and with color cosmetic lens....
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...[129] IIITD CLI [128], ND-Contact Lens [147] Investigate the role of contact lenses in obfuscation of iris texture...
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References

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Open Access

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Journal Article•DOI•

Demodulation by complex-valued wavelets for stochastic pattern recognition

[...]

John Daugman¹•Institutions (1)

University of Cambridge¹

01 Mar 2003-International Journal of Wavelets, Multiresolution and Information Processing

TL;DR: This paper discusses exploitation of this statistical principle, combined with wavelet image coding methods to extract phase descriptions of incoherent patterns from stochastic signals.

...read moreread less

Abstract: Samples from stochastic signals having sufficient complexity need reveal only a little unexpected shared structure, in order to reject the hypothesis that they are independent. The mere failure of a test of statistical independence can thereby serve as a basis for recognizing stochastic patterns, provided they possess enough degrees-of-freedom, because all unrelated ones would pass such a test. This paper discusses exploitation of this statistical principle, combined with wavelet image coding methods to extract phase descriptions of incoherent patterns. Demodulation and coarse quantization of the phase information creates decision environments characterized by well-separated clusters, and this lends itself to rapid and reliable pattern recognition.

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338 citations

"Revisiting iris recognition with co..." refers background in this paper

...The discussion about fake iris images and images with contact lens effect was first initiated by Daugman [5] where frequency spectrum analysis was proposed to countermeasure against subterfuge....
[...]

Journal Article•DOI•

Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing

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Mayank Vatsa¹, Richa Singh¹, Afzel Noore¹•Institutions (1)

West Virginia University¹

01 Aug 2008

TL;DR: This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition.

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Abstract: This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition. A curve evolution approach is proposed to effectively segment a nonideal iris image using the modified Mumford-Shah functional. Different enhancement algorithms are concurrently applied on the segmented iris image to produce multiple enhanced versions of the iris image. A support-vector-machine-based learning algorithm selects locally enhanced regions from each globally enhanced image and combines these good-quality regions to create a single high-quality iris image. Two distinct features are extracted from the high-quality iris image. The global textural feature is extracted using the 1-D log polar Gabor transform, and the local topological feature is extracted using Euler numbers. An intelligent fusion algorithm combines the textural and topological matching scores to further improve the iris recognition performance and reduce the false rejection rate, whereas an indexing algorithm enables fast and accurate iris identification. The verification and identification performance of the proposed algorithms is validated and compared with other algorithms using the CASIA Version 3, ICE 2005, and UBIRIS iris databases.

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285 citations

"Revisiting iris recognition with co..." refers methods in this paper

...Since the commercial systems do not provide the flexibility of extracting the active contour based segmentation of iris regions, we have used the algorithm proposed in [12]....
[...]

Proceedings Article•DOI•

Counterfeit iris detection based on texture analysis

[...]

Zhuoshi Wei¹, Xianchao Qiu¹, Zhenan Sun¹, Tieniu Tan¹•Institutions (1)

Chinese Academy of Sciences¹

01 Dec 2008

TL;DR: This paper proposes three measures to detect fake iris: measuring iris edge sharpness, applying Iris-Texton feature for characterizing the visual primitives of iris textures and using selected features based on co-occurrence matrix (CM).

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Abstract: This paper addresses the issue of counterfeit iris detection, which is a liveness detection problem in biometrics. Fake iris mentioned here refers to iris wearing color contact lens with textures printed onto them. We propose three measures to detect fake iris: measuring iris edge sharpness, applying Iris-Texton feature for characterizing the visual primitives of iris textures and using selected features based on co-occurrence matrix (CM). Extensive testing is carried out on two datasets containing different types of contact lens with totally 640 fake iris images, which demonstrates that Iris-Texton and CM features are effective and robust in anticounterfeit iris. Detailed comparisons with two state-of-the-art methods are also presented, showing that the proposed iris edge sharpness measure acquires a comparable performance with these two methods, while Iris-Texton and CM features outperform the state-of-the-art.

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143 citations

"Revisiting iris recognition with co..." refers background or methods in this paper

...Confusion matrix for normal (without lens) classification problem using edge sharpness [14]....
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...Confusion matrix of lens detection using the textural features [14]....
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...Examples of correct (a: without lens and b: colored lens) and incorrect classifications (c: without lens as colored lens and d: colored lens as without lens) using co-occurrence matrix [14]....
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...To test this hypothesis, we have evaluated the performance of four existing techniques: (1) iris edge sharpness [14], (2) textural features based on co-occurrence matrix [14], (3) GLCM based analysis [7], and (4) local binary pattern (LBP) and SVM based classification....
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...[14] used three different features to detect lens in iris images....
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Journal Article•DOI•

Pupil dilation degrades iris biometric performance

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Karen Hollingsworth¹, Kevin W. Bowyer¹, Patrick J. Flynn¹•Institutions (1)

University of Notre Dame¹

01 Jan 2009-Computer Vision and Image Understanding

TL;DR: It is found that when the degree of dilation is similar at enrollment and recognition, comparisons involving highly dilated pupils result in worse recognition performance than comparisons involving constricted pupils, and it is recommended that a measure of pupil dilation be kept as meta-data for every iris code.

...read moreread less

132 citations

"Revisiting iris recognition with co..." refers background in this paper

...Though iris features are considered to be unique, recent research results suggest that they are affected by several covariates such as pupil dilation [8] and sensor interoperability [1, 4]....
[...]

Proceedings Article•DOI•

Contact Lens Detection Based on Weighted LBP

[...]

Hui Zhang¹, Zhenan Sun², Tieniu Tan²•Institutions (2)

Shanghai Institute of Technology¹, Chinese Academy of Sciences²

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.

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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.

...read moreread less

120 citations