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Greatest papers with code
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.
Deep Learning-Based Feature Extraction in Iris Recognition: Use Existing Models, Fine-tune or Train From Scratch?
Features are extracted from each convolutional layer and the classification accuracy achieved by a Support Vector Machine is measured on a dataset that is disjoint from the samples used in training of the ResNet-50 model.
Open Source Iris Recognition Hardware and Software with Presentation Attack Detection
• CVRL/RaspberryPiOpenSourceIris
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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.
Open Source Presentation Attack Detection Baseline for Iris Recognition
• CVRL/RaspberryPiOpenSourceIris
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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.
A Resource-Efficient Embedded Iris Recognition System Using Fully Convolutional Networks
To attain accurate and efficient FCN models, we propose a three-step SW/HW co-design methodology consisting of FCN architectural exploration, precision quantization, and hardware acceleration.
ThirdEye: Triplet Based Iris Recognition without Normalization
• sohaib50k/ThirdEye---Iris-recognition-using-triplets
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We observe equal error rates of 1. 32%, 9. 20%, and 0. 59% on the ND-0405, UbirisV2, and IITD datasets respectively.
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.
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).
CROSS-DOMAIN IRIS PRESENTATION ATTACK DETECTION IRIS RECOGNITION MULTI-VIEW LEARNING
Resist : Reconstruction of irises from templates
• sohaib50k/RESIST-Iris-template-reconstruction
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This template is stored on a system.
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.
DOMAIN ADAPTATION EYE TRACKING IRIS RECOGNITION TEXTURE CLASSIFICATION