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Found 13 papers, 3 papers with code
Iris Liveness Detection Competition (LivDet-Iris) -- The 2023 Edition
New elements in this fifth competition include (1) GAN-generated iris images as a category of presentation attack instruments (PAI), and (2) an evaluation of human accuracy at detecting PAI as a reference benchmark.
Teaching AI to Teach: Leveraging Limited Human Salience Data Into Unlimited Saliency-Based Training
We compare the accuracy achieved by our teacher-student training paradigm with (1) training using all available human salience annotations, and (2) using all available training data without human salience annotations.
Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models
In this paper, we propose five novel methods of leveraging model salience to explain a model behavior at scale.
State Of The Art In Open-Set Iris Presentation Attack Detection
We have curated the largest publicly-available image dataset for this problem, drawing from 26 benchmarks previously released by various groups, and adding 150, 000 images being released with the journal version of this paper, to create a set of 450, 000 images representing authentic iris and seven types of presentation attack instrument (PAI).
The Value of AI Guidance in Human Examination of Synthetically-Generated Faces
Face image synthesis has progressed beyond the point at which humans can effectively distinguish authentic faces from synthetically generated ones.
Human Saliency-Driven Patch-based Matching for Interpretable Post-mortem Iris Recognition
Forensic iris recognition, as opposed to live iris recognition, is an emerging research area that leverages the discriminative power of iris biometrics to aid human examiners in their efforts to identify deceased persons.
Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition
In this paper, we present an end-to-end deep learning-based method for postmortem iris segmentation and recognition with a special visualization technique intended to support forensic human examiners in their efforts.
CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning
This new approach incorporates human-annotated saliency maps into a loss function that guides the model's learning to focus on image regions that humans deem salient for the task.
Human-Aided Saliency Maps Improve Generalization of Deep Learning
One ongoing challenge is how to achieve the greatest accuracy in cases where training data is limited.
Iris Liveness Detection Competition (LivDet-Iris) -- The 2020 Edition
Launched in 2013, LivDet-Iris is an international competition series open to academia and industry with the aim to assess and report advances in iris Presentation Attack Detection (PAD).
Iris Presentation Attack Detection: Where Are We Now?
As the popularity of iris recognition systems increases, the importance of effective security measures against presentation attacks becomes paramount.
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.
Are Gabor Kernels Optimal for Iris Recognition?
We use (on purpose) a single-layer convolutional neural network as it mimics an iris code-based algorithm.
