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20182026
most citedEnsemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection

59 citations · 238 across the 49 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.CV2021

Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition

Andrey Kuehlkamp, Aidan Boyd, Adam Czajka +4

Iris recognition of living individuals is a mature biometric modality that has been adopted globally from governmental ID programs, border crossing, voter registration and de-dupli…

cs.CV2021

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?…

cs.CV2021

Digital and Physical-World Attacks on Remote Pulse Detection

Jeremy Speth, Nathan Vance, Patrick Flynn +2

Remote photoplethysmography (rPPG) is a technique for estimating blood volume changes from reflected light without the need for a contact sensor. We present the first examples of p…

cs.CV2021

Deception Detection and Remote Physiological Monitoring: A Dataset and Baseline Experimental Results

Jeremy Speth, Nathan Vance, Adam Czajka +3

We present the Deception Detection and Physiological Monitoring (DDPM) dataset and initial baseline results on this dataset. Our application context is an interview scenario in whi…

cs.CV2021

Human-Aided Saliency Maps Improve Generalization of Deep Learning

Aidan Boyd, Kevin Bowyer, Adam Czajka

Deep learning has driven remarkable accuracy increases in many computer vision problems. One ongoing challenge is how to achieve the greatest accuracy in cases where training data…

cs.CV2021

Remote Pulse Estimation in the Presence of Face Masks

Jeremy Speth, Nathan Vance, Patrick Flynn +2

Remote photoplethysmography (rPPG), a family of techniques for monitoring blood volume changes, may be especially useful for widespread contactless health monitoring using face vid…