Iris Recognition
21 papers with code • 0 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Iris Recognition
Libraries
Use these libraries to find Iris Recognition models and implementationsMost implemented papers
Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition
One important point is that all applications of BSIF in iris recognition have used the original BSIF filters, which were trained on image patches extracted from natural images.
Open Source Presentation Attack Detection Baseline for Iris Recognition
This paper proposes the first, known to us, open source presentation attack detection (PAD) solution to distinguish between authentic iris images (possibly wearing clear contact lenses) and irises with textured contact lenses.
D-NetPAD: An Explainable and Interpretable Iris Presentation Attack Detector
An iris recognition system is vulnerable to presentation attacks, or PAs, where an adversary presents artifacts such as printed eyes, plastic eyes, or cosmetic contact lenses to circumvent the system.
Open Source Iris Recognition Hardware and Software with Presentation Attack Detection
This paper proposes the first known to us open source hardware and software iris recognition system with presentation attack detection (PAD), which can be easily assembled for about 75 USD using Raspberry Pi board and a few peripherals.
Analyzing Covariate Influence on Gender and Race Prediction from Near-Infrared Ocular Images
Recent research has explored the possibility of automatically deducing information such as gender, age and race of an individual from their biometric data.
Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection
The adoption of large-scale iris recognition systems around the world has brought to light the importance of detecting presentation attack images (textured contact lenses and printouts).
Iris Recognition with Image Segmentation Employing Retrained Off-the-Shelf Deep Neural Networks
This paper offers three new, open-source, deep learning-based iris segmentation methods, and the methodology how to use irregular segmentation masks in a conventional Gabor-wavelet-based iris recognition.
Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation
We propose to use deep learning-based iris segmentation models to extract highly irregular iris texture areas in post-mortem iris images.
Gender Classification from Iris Texture Images Using a New Set of Binary Statistical Image Features
This paper explores the use of a Binary Statistical Features (BSIF) algorithm for classifying gender from iris texture images captured with NIR sensors.
ThirdEye: Triplet Based Iris Recognition without Normalization
We observe equal error rates of 1. 32%, 9. 20%, and 0. 59% on the ND-0405, UbirisV2, and IITD datasets respectively.