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Found 8 papers, 3 papers with code
EyePreserve: Identity-Preserving Iris Synthesis
Synthesis of same-identity biometric iris images, both for existing and non-existing identities while preserving the identity across a wide range of pupil sizes, is complex due to intricate iris muscle constriction mechanism, requiring a precise model of iris non-linear texture deformations to be embedded into the synthesis pipeline.
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.
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.
Haven't I Seen You Before? Assessing Identity Leakage in Synthetic Irises
Generative Adversarial Networks (GANs) have proven to be a preferred method of synthesizing fake images of objects, such as faces, animals, and automobiles.
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.
DeformIrisNet: An Identity-Preserving Model of Iris Texture Deformation
Nonlinear iris texture deformations due to pupil size variations are one of the main factors responsible for within-class variance of genuine comparison scores in iris recognition.
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.
This Face Does Not Exist ... But It Might Be Yours! Identity Leakage in Generative Models
This raises privacy-related questions, but also stimulates discussions of (a) the face manifold's characteristics in the feature space and (b) how to create generative models that do not inadvertently reveal identity information of real subjects whose images were used for training.
