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 Carlos Aravena Daniel Schulz
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
supervision network, showed the best-weighted accuracy results (average
classification error rate of 37.31%), while the Beijing University of Civil
Engineering and Architecture's algorithm won when equal weights for each PAI
were given (average classification rate of 22.15%). These results suggest that
iris PAD is still a challenging problem.