110 research outputs found
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
LivDet 2017 Fingerprint Liveness Detection Competition 2017
Fingerprint Presentation Attack Detection (FPAD) deals with distinguishing
images coming from artificial replicas of the fingerprint characteristic, made
up of materials like silicone, gelatine or latex, and images coming from alive
fingerprints. Images are captured by modern scanners, typically relying on
solid-state or optical technologies. Since from 2009, the Fingerprint Liveness
Detection Competition (LivDet) aims to assess the performance of the
state-of-the-art algorithms according to a rigorous experimental protocol and,
at the same time, a simple overview of the basic achievements. The competition
is open to all academics research centers and all companies that work in this
field. The positive, increasing trend of the participants number, which
supports the success of this initiative, is confirmed even this year: 17
algorithms were submitted to the competition, with a larger involvement of
companies and academies. This means that the topic is relevant for both sides,
and points out that a lot of work must be done in terms of fundamental and
applied research.Comment: presented at ICB 201
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
Textural features for fingerprint liveness detection
The main topic ofmy research during these three years concerned biometrics and in particular
the Fingerprint Liveness Detection (FLD), namely the recognition of fake fingerprints.
Fingerprints spoofing is a topical issue as evidenced by the release of the latest iPhone and
Samsung Galaxy models with an embedded fingerprint reader as an alternative to passwords.
Several videos posted on YouTube show how to violate these devices by using fake
fingerprints which demonstrated how the problemof vulnerability to spoofing constitutes a
threat to the existing fingerprint recognition systems.
Despite the fact that many algorithms have been proposed so far, none of them showed
the ability to clearly discriminate between real and fake fingertips. In my work, after a study
of the state-of-the-art I paid a special attention on the so called textural algorithms. I first
used the LBP (Local Binary Pattern) algorithm and then I worked on the introduction of the
LPQ (Local Phase Quantization) and the BSIF (Binarized Statistical Image Features) algorithms
in the FLD field.
In the last two years I worked especially on what we called the “user specific” problem.
In the extracted features we noticed the presence of characteristic related not only to the
liveness but also to the different users. We have been able to improve the obtained results
identifying and removing, at least partially, this user specific characteristic.
Since 2009 the Department of Electrical and Electronic Engineering of the University of
Cagliari and theDepartment of Electrical and Computer Engineering of the ClarksonUniversity
have organized the Fingerprint Liveness Detection Competition (LivDet). I have been
involved in the organization of both second and third editions of the Fingerprint Liveness
Detection Competition (LivDet 2011 and LivDet 2013) and I am currently involved in the acquisition
of live and fake fingerprint that will be inserted in three of the LivDet 2015 datasets
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