Proceedings ArticleDOI
Revisiting iris recognition with color cosmetic contact lenses
Naman Kohli,Daksha Yadav,Mayank Vatsa,Richa Singh +3 more
- pp 1-7
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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.read more
Citations
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Proceedings ArticleDOI
Detecting medley of iris spoofing attacks using DESIST
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.
Proceedings ArticleDOI
Variation in accuracy of textured contact lens detection based on sensor and lens pattern
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.
Proceedings ArticleDOI
An Approach to Iris Contact Lens Detection Based on Deep Image Representations
TL;DR: This work uses a convolutional network to build a deep image representation and an additional fully-connected single layer with soft max regression for classification for Iris spoofing detection, which can achieve a 30% performance gain over SOTA on two public iris image databases for contact lens detection.
Journal ArticleDOI
A deep learning approach for iris sensor model identification
TL;DR: The proposed algorithm based on convolutional neural networks for iris sensor model identification outperforms the state-of-the art approaches used for the model identification task and is tested on several public iris databases.
Proceedings ArticleDOI
ContlensNet: Robust Iris Contact Lens Detection Using Deep Convolutional Neural Networks
TL;DR: Improved performance of the proposed scheme for detection to detecting a contact lens using Deep Convolutional Neural Network is demonstrated with an average performance improvement of more than 10% in Correct Classification Rate (CCR%) when compared with eight different state-of-the-art contact lens detection systems.
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Proceedings ArticleDOI
Contact Lens Detection Based on Weighted LBP
Hui Zhang,Zhenan Sun,Tieniu Tan +2 more
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.