Iris Recognition
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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.
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.
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.
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.
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.
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