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Proceedings ArticleDOI

Revisiting iris recognition with color cosmetic contact lenses

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TLDR
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.

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Citations
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Proceedings ArticleDOI

Synthesizing Iris Images Using RaSGAN With Application in Presentation Attack Detection

TL;DR: A new technique for generating synthetic iris images is designed and its potential for presentation attack detection (PAD) is demonstrated and the viability of using these synthetic images to train a PAD system that can generalize well to "unseen" attacks is demonstrated.

Face anti-spoofing via motion magnification and multifeature videolet aggregation

TL;DR: A new framework for face spoofing detection in videos using motion magnification and multifeature evidence aggregation in a windowed fashion is presented, which yields state-of-the-art performance and robust generalizability with low computational complexity.
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Using iris and sclera for detection and classification of contact lenses

TL;DR: A machine-learning approach for this task, based on expressive local image descriptors, shows that the proposed classification method based on a dense scale invariant descriptor outperforms all the reference techniques.
Proceedings ArticleDOI

Synthetic iris presentation attack using iDCGAN

TL;DR: In this paper, a novel iris presentation attack using deep learning based synthetic iris generation is presented. But, the attack is limited to textured contact lenses and print attacks.
Proceedings ArticleDOI

Detecting Textured Contact Lens in Uncontrolled Environment Using DensePAD

TL;DR: A new Unconstrained Multi-sensor Iris Presentation Attack (UnMIPA) database is created and a novel algorithm, DensePAD, which utilizes DenseNet based convolutional neural network architecture for iris presentation attack detection is presented.
References
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Demodulation by complex-valued wavelets for stochastic pattern recognition

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.
Journal ArticleDOI

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

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.
Proceedings ArticleDOI

Counterfeit iris detection based on texture analysis

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).
Journal ArticleDOI

Pupil dilation degrades iris biometric performance

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.
Proceedings ArticleDOI

Contact Lens Detection Based on Weighted LBP

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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