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Found 28 papers, 7 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.
Beard Segmentation and Recognition Bias
To investigate the effect of facial hair in a rigorous manner, we first created a set of fine-grained facial hair annotations to train a segmentation model and evaluate its accuracy across African-American and Caucasian face images.
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.
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.
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).
Analyzing the Impact of Shape & Context on the Face Recognition Performance of Deep Networks
In this article, we analyze how changing the underlying 3D shape of the base identity in face images can distort their overall appearance, especially from the perspective of deep face recognition.
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.
A Study of the Human Perception of Synthetic Faces
In this paper, we introduce a study of the human perception of synthetic faces generated using different strategies including a state-of-the-art deep learning-based GAN model.
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.
Going Deeper Into Face Detection: A Survey
In this work, we provide a detailed overview of some of the most representative deep learning based face detection methods by grouping them into a few major categories, and present their core architectural designs and accuracies on popular benchmarks.
Remote Pulse Estimation in the Presence of Face Masks
Remote photoplethysmography (rPPG), a family of techniques for monitoring blood volume changes, may be especially useful for widespread contactless health monitoring using face video from consumer-grade visible-light cameras.
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).
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.
Learning Transformation-Aware Embeddings for Image Forensics
To establish the efficacy of the proposed approach, comparisons with state-of-the-art handcrafted and deep learning-based descriptors, and image matching approaches are made.
Characterizing the Variability in Face Recognition Accuracy Relative to Race
For a fixed decision threshold, the African-American image cohort has a higher false match rate and a lower false non-match rate.
Dynamic Spatial Verification for Large-Scale Object-Level Image Retrieval
Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks.
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.
Predicting Gender from Iris Texture May Be Harder Than It Seems
Predicting gender from iris images has been reported by several researchers as an application of machine learning in biometrics.
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
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Found a good match: should I keep searching? - Accuracy and Performance in Iris Matching Using 1-to-First Search
Iris recognition is used in many applications around the world, with enrollment sizes as large as over one billion persons in India's Aadhaar program.
Provenance Filtering for Multimedia Phylogeny
Departing from traditional digital forensics modeling, which seeks to analyze single objects in isolation, multimedia phylogeny analyzes the evolutionary processes that influence digital objects and collections over time.
U-Phylogeny: Undirected Provenance Graph Construction in the Wild
Deriving relationships between images and tracing back their history of modifications are at the core of Multimedia Phylogeny solutions, which aim to combat misinformation through doctored visual media.
Spotting the Difference: Context Retrieval and Analysis for Improved Forgery Detection and Localization
As image tampering becomes ever more sophisticated and commonplace, the need for image forensics algorithms that can accurately and quickly detect forgeries grows.
Gender-From-Iris or Gender-From-Mascara?
Predicting a person's gender based on the iris texture has been explored by several researchers.
