89 research outputs found
Iris Liveness Detection Competition (LivDet-Iris) -- The 2020 Edition
Launched in 2013, LivDet-Iris is an international competition series open to
academia and industry with the aim to assess and report advances in iris
Presentation Attack Detection (PAD). This paper presents results from the
fourth competition of the series: LivDet-Iris 2020. This year's competition
introduced several novel elements: (a) incorporated new types of attacks
(samples displayed on a screen, cadaver eyes and prosthetic eyes), (b)
initiated LivDet-Iris as an on-going effort, with a testing protocol available
now to everyone via the Biometrics Evaluation and Testing
(BEAT)(https://www.idiap.ch/software/beat/) open-source platform to facilitate
reproducibility and benchmarking of new algorithms continuously, and (c)
performance comparison of the submitted entries with three baseline methods
(offered by the University of Notre Dame and Michigan State University), and
three open-source iris PAD methods available in the public domain. The best
performing entry to the competition reported a weighted average APCER of
59.10\% and a BPCER of 0.46\% over all five attack types. This paper serves as
the latest evaluation of iris PAD on a large spectrum of presentation attack
instruments.Comment: 9 pages, 3 figures, 3 tables, Accepted for presentation at
International Joint Conference on Biometrics (IJCB 2020
Biometric presentation attack detection: beyond the visible spectrum
The increased need for unattended authentication in
multiple scenarios has motivated a wide deployment of biometric
systems in the last few years. This has in turn led to the
disclosure of security concerns specifically related to biometric
systems. Among them, presentation attacks (PAs, i.e., attempts
to log into the system with a fake biometric characteristic or
presentation attack instrument) pose a severe threat to the
security of the system: any person could eventually fabricate
or order a gummy finger or face mask to impersonate someone
else. In this context, we present a novel fingerprint presentation
attack detection (PAD) scheme based on i) a new capture device
able to acquire images within the short wave infrared (SWIR)
spectrum, and i i) an in-depth analysis of several state-of-theart
techniques based on both handcrafted and deep learning
features. The approach is evaluated on a database comprising
over 4700 samples, stemming from 562 different subjects and
35 different presentation attack instrument (PAI) species. The
results show the soundness of the proposed approach with a
detection equal error rate (D-EER) as low as 1.35% even in a
realistic scenario where five different PAI species are considered
only for testing purposes (i.e., unknown attacks
LivDet in Action - Fingerprint Liveness Detection Competition 2019
The International Fingerprint liveness Detection Competition (LivDet) is an
open and well-acknowledged meeting point of academies and private companies
that deal with the problem of distinguishing images coming from reproductions
of fingerprints made of artificial materials and images relative to real
fingerprints. In this edition of LivDet we invited the competitors to propose
integrated algorithms with matching systems. The goal was to investigate at
which extent this integration impact on the whole performance. Twelve
algorithms were submitted to the competition, eight of which worked on
integrated systems.Comment: Preprint version of a paper accepted at ICB 201
- …