100 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
Fusion of fingerprint presentation attacks detection and matching: a real approach from the LivDet perspective
The liveness detection ability is explicitly required for current personal verification systems in many security applications. As a matter of fact, the project of any biometric verification system cannot ignore the vulnerability to spoofing or presentation attacks (PAs), which must be addressed by effective countermeasures from the beginning of the design process. However, despite significant improvements, especially by adopting deep learning approaches to fingerprint Presentation Attack Detectors (PADs), current research did not state much about their effectiveness when embedded in fingerprint verification systems. We believe that the lack of works is explained by the lack of instruments to investigate the problem, that is, modelling the cause-effect relationships when two systems (spoof detection and matching) with non-zero error rates are integrated.
To solve this lack of investigations in the literature, we present in this PhD thesis a novel performance simulation model based on the probabilistic relationships between the Receiver Operating Characteristics (ROC) of the two systems when implemented sequentially. As a matter of fact, this is the most straightforward, flexible, and widespread approach. We carry out simulations on the PAD algorithms’ ROCs submitted to the editions of LivDet 2017-2019, the NIST Bozorth3, and the top-level VeriFinger 12.0 matchers. With the help of this simulator, the overall system performance can be predicted before actual implementation, thus simplifying the process of setting the best trade-off among error rates.
In the second part of this thesis, we exploit this model to define a practical evaluation criterion to assess whether operational points of the PAD exist that do not alter the expected or previous performance given by the verification system alone. Experimental simulations coupled with the theoretical expectations confirm that this trade-off allows a complete view of the sequential embedding potentials worthy of being extended to other integration approaches
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