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
A Benchmark for Iris Location and a Deep Learning Detector Evaluation
Evair Severo,Rayson Laroca,Cides S. Bezerra,Luiz A. Zanlorensi,Daniel Weingaertner,Gladston Moreira,David Menotti +6 more
TL;DR: In this article, the authors define the iris location problem as the delimitation of the smallest squared window that encompasses the entire iris region and compare the classical and outstanding Daugman iris localization approach with two window based detectors: 1) a sliding window detector based on features from Histogram of Oriented Gradients (HOG) and a linear Support Vector Machines (SVM) classifier; 2) a deep learning based detector fine-tuned from YOLO object detector.
Journal ArticleDOI
Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection
TL;DR: In this paper, a new approach for iris presentation attack detection was proposed by exploring combinations of Convolutional Neural Networks (CNNs) and transformed input spaces through binarized statistical image features (BSIF).
Proceedings ArticleDOI
Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Network
TL;DR: Zhang et al. as discussed by the authors proposed an attention-based deep pixel-wise bi-nary supervision (A-PBS) method to detect iris presentation attack detection.
Proceedings ArticleDOI
A Benchmark for Iris Location and a Deep Learning Detector Evaluation
Evair Severo,Rayson Laroca,Cides S. Bezerra,Luiz A. Zanlorensi,Daniel Weingaertner,Gladston Moreira,David Menotti +6 more
TL;DR: Experimental results showed that the deep learning based detector outperforms the other ones in terms of accuracy and runtime (GPUs version) and should be chosen whenever possible.
Posted Content
Presentation Attack Detection for Iris Recognition: An Assessment of the State of the Art.
Adam Czajka,Kevin W. Bowyer +1 more
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.
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Proceedings ArticleDOI
Contact Lens Detection Based on Weighted LBP
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