2 citations · 5 across the 13 of their papers we have counts for
16 papers
Beyond Mortality: Advancements in Post-Mortem Iris Recognition through Data Collection and Computer-Aided Forensic Examination
Rasel Ahmed Bhuiyan, Parisa Farmanifard, Renu Sharma +7
Post-mortem iris recognition brings both hope to the forensic community (a short-term but accurate and fast means of verifying identity) as well as concerns to society (its potenti…
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…
Increasing Interpretability of Neural Networks By Approximating Human Visual Saliency
Aidan Boyd, Mohamed Trabelsi, Huseyin Uzunalioglu +1
Understanding specifically where a model focuses on within an image is critical for human interpretability of the decision-making process. Deep learning-based solutions are prone t…
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…
Teaching AI to Teach: Leveraging Limited Human Salience Data Into Unlimited Saliency-Based Training
Colton R. Crum, Aidan Boyd, Kevin Bowyer +1
Machine learning models have shown increased accuracy in classification tasks when the training process incorporates human perceptual information. However, a challenge in training…
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…