Iris Liveness Detection Competition (LivDet-Iris) -- The 2023 Edition
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

TL;DR
The 2023 LivDet-Iris competition evaluated iris presentation attack detection methods, including GAN-generated images, comparing algorithm performance and human accuracy, revealing ongoing challenges in iris PAD.
Contribution
This paper introduces new PAI categories, including GAN-generated images, and provides a comprehensive benchmark of algorithms and human performance in iris PAD.
Findings
Fraunhofer IGD's algorithm achieved the lowest error rate.
Beijing University of Civil Engineering and Architecture's algorithm had the highest accuracy with equal PAI weighting.
Iris PAD remains a challenging problem.
Abstract
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 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. Clarkson University and the University of Notre Dame contributed image datasets for the competition, composed of samples representing seven different PAI categories, as well as baseline PAD algorithms. Fraunhofer IGD, Beijing University of Civil Engineering and Architecture, and Hochschule Darmstadt contributed results for a total of eight PAD algorithms to the competition. Accuracy results are analyzed by different PAI types, and compared to human accuracy. Overall, the Fraunhofer IGD algorithm, using an attention-based pixel-wise binary…
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Taxonomy
TopicsBiometric Identification and Security
