no code implementations • 19 Dec 2023 • Siamul Karim Khan, Patrick Tinsley, Mahsa Mitcheff, Patrick Flynn, Kevin W. Bowyer, Adam Czajka
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.
no code implementations • 6 Oct 2023 • Patrick Tinsley, Sandip Purnapatra, Mahsa Mitcheff, Aidan Boyd, Colton Crum, Kevin Bowyer, Patrick Flynn, Stephanie Schuckers, Adam Czajka, Meiling Fang, Naser Damer, Xingyu Liu, Caiyong Wang, Xianyun Sun, Zhaohua Chang, Xinyue Li, Guangzhe Zhao, Juan Tapia, Christoph Busch, Carlos Aravena, Daniel Schulz
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.
no code implementations • 21 Mar 2023 • Colton Crum, Patrick Tinsley, Aidan Boyd, Jacob Piland, Christopher Sweet, Timothy Kelley, Kevin Bowyer, Adam Czajka
In this paper, we propose five novel methods of leveraging model salience to explain a model behavior at scale.
no code implementations • 3 Nov 2022 • Patrick Tinsley, Adam Czajka, Patrick Flynn
Generative Adversarial Networks (GANs) have proven to be a preferred method of synthesizing fake images of objects, such as faces, animals, and automobiles.
no code implementations • 22 Aug 2022 • Aidan Boyd, Patrick Tinsley, Kevin Bowyer, Adam Czajka
Face image synthesis has progressed beyond the point at which humans can effectively distinguish authentic faces from synthetically generated ones.
1 code implementation • 18 Jul 2022 • Siamul Karim Khan, Patrick Tinsley, Adam Czajka
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.
1 code implementation • 1 Dec 2021 • Aidan Boyd, Patrick Tinsley, Kevin Bowyer, Adam Czajka
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.
1 code implementation • 10 Dec 2020 • Patrick Tinsley, Adam Czajka, Patrick Flynn
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.