2 citations · 4 across the 10 of their papers we have counts for
10 papers · 1 filter
Training Better Deep Learning Models Using Human Saliency
Aidan Boyd, Patrick Tinsley, Kevin W. Bowyer +1
This work explores how human judgement about salient regions of an image can be introduced into deep convolutional neural network (DCNN) training. Traditionally, training of DCNNs…
Privacy-Safe Iris Presentation Attack Detection
Mahsa Mitcheff, Patrick Tinsley, Adam Czajka
This paper proposes a framework for a privacy-safe iris presentation attack detection (PAD) method, designed solely with synthetically-generated, identity-leakage-free iris images.…
EyePreserve: Identity-Preserving Iris Synthesis
Siamul Karim Khan, Patrick Tinsley, Mahsa Mitcheff +3
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…
Iris Liveness Detection Competition (LivDet-Iris) -- The 2023 Edition
Patrick Tinsley, Sandip Purnapatra, Mahsa Mitcheff +18
This paper describes the results of the 2023 edition of the ''LivDet'' series of iris presentation attack detection (PAD) competitions. New elements in this fifth competition inclu…
Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models
Colton Crum, Patrick Tinsley, Aidan Boyd +5
The performance of convolutional neural networks has continued to improve over the last decade. At the same time, as model complexity grows, it becomes increasingly more difficult…
Haven't I Seen You Before? Assessing Identity Leakage in Synthetic Irises
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. It is not surprising th…
The Value of AI Guidance in Human Examination of Synthetically-Generated Faces
Aidan Boyd, Patrick Tinsley, Kevin Bowyer +1
Face image synthesis has progressed beyond the point at which humans can effectively distinguish authentic faces from synthetically generated ones. Recently developed synthetic fac…
DeformIrisNet: An Identity-Preserving Model of Iris Texture Deformation
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.…
CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning
Aidan Boyd, Patrick Tinsley, Kevin Bowyer +1
Can deep learning models achieve greater generalization if their training is guided by reference to human perceptual abilities? And how can we implement this in a practical manner?…
This Face Does Not Exist ... But It Might Be Yours! Identity Leakage in Generative Models
Patrick Tinsley, Adam Czajka, Patrick Flynn
Generative adversarial networks (GANs) are able to generate high resolution photo-realistic images of objects that "do not exist." These synthetic images are rather difficult to de…