103 research outputs found
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
Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey
The vulnerabilities of fingerprint authentication systems have raised
security concerns when adapting them to highly secure access-control
applications. Therefore, Fingerprint Presentation Attack Detection (FPAD)
methods are essential for ensuring reliable fingerprint authentication. Owing
to the lack of generation capacity of traditional handcrafted based approaches,
deep learning-based FPAD has become mainstream and has achieved remarkable
performance in the past decade. Existing reviews have focused more on
hand-cratfed rather than deep learning-based methods, which are outdated. To
stimulate future research, we will concentrate only on recent
deep-learning-based FPAD methods. In this paper, we first briefly introduce the
most common Presentation Attack Instruments (PAIs) and publicly available
fingerprint Presentation Attack (PA) datasets. We then describe the existing
deep-learning FPAD by categorizing them into contact, contactless, and
smartphone-based approaches. Finally, we conclude the paper by discussing the
open challenges at the current stage and emphasizing the potential future
perspective.Comment: 29 pages, submitted to ACM computing survey journa
Feature Fusion for Fingerprint Liveness Detection
For decades, fingerprints have been the most widely used biometric trait in identity
recognition systems, thanks to their natural uniqueness, even in rare cases such as
identical twins. Recently, we witnessed a growth in the use of fingerprint-based
recognition systems in a large variety of devices and applications. This, as a consequence,
increased the benefits for offenders capable of attacking these systems. One
of the main issues with the current fingerprint authentication systems is that, even
though they are quite accurate in terms of identity verification, they can be easily
spoofed by presenting to the input sensor an artificial replica of the fingertip skin’s
ridge-valley patterns.
Due to the criticality of this threat, it is crucial to develop countermeasure
methods capable of facing and preventing these kind of attacks. The most effective
counter–spoofing methods are those trying to distinguish between a "live" and a
"fake" fingerprint before it is actually submitted to the recognition system. According
to the technology used, these methods are mainly divided into hardware and software-based
systems. Hardware-based methods rely on extra sensors to gain more pieces
of information regarding the vitality of the fingerprint owner. On the contrary,
software-based methods merely rely on analyzing the fingerprint images acquired
by the scanner. Software-based methods can then be further divided into dynamic,
aimed at analyzing sequences of images to capture those vital signs typical of a real
fingerprint, and static, which process a single fingerprint impression. Among these
different approaches, static software-based methods come with three main benefits.
First, they are cheaper, since they do not require the deployment of any additional
sensor to perform liveness detection. Second, they are faster since the information
they require is extracted from the same input image acquired for the identification
task. Third, they are potentially capable of tackling novel forms of attack through an
update of the software. The interest in this type of counter–spoofing methods is at the basis of this
dissertation, which addresses the fingerprint liveness detection under a peculiar
perspective, which stems from the following consideration. Generally speaking, this
problem has been tackled in the literature with many different approaches. Most of
them are based on first identifying the most suitable image features for the problem
in analysis and, then, into developing some classification system based on them. In
particular, most of the published methods rely on a single type of feature to perform
this task. Each of this individual features can be more or less discriminative and often
highlights some peculiar characteristics of the data in analysis, often complementary
with that of other feature. Thus, one possible idea to improve the classification
accuracy is to find effective ways to combine them, in order to mutually exploit their
individual strengths and soften, at the same time, their weakness. However, such a
"multi-view" approach has been relatively overlooked in the literature.
Based on the latter observation, the first part of this work attempts to investigate
proper feature fusion methods capable of improving the generalization and robustness
of fingerprint liveness detection systems and enhance their classification strength.
Then, in the second part, it approaches the feature fusion method in a different way,
that is by first dividing the fingerprint image into smaller parts, then extracting an
evidence about the liveness of each of these patches and, finally, combining all these
pieces of information in order to take the final classification decision.
The different approaches have been thoroughly analyzed and assessed by comparing
their results (on a large number of datasets and using the same experimental
protocol) with that of other works in the literature. The experimental results discussed
in this dissertation show that the proposed approaches are capable of obtaining
state–of–the–art results, thus demonstrating their effectiveness
An Open Patch Generator based Fingerprint Presentation Attack Detection using Generative Adversarial Network
The low-cost, user-friendly, and convenient nature of Automatic Fingerprint
Recognition Systems (AFRS) makes them suitable for a wide range of
applications. This spreading use of AFRS also makes them vulnerable to various
security threats. Presentation Attack (PA) or spoofing is one of the threats
which is caused by presenting a spoof of a genuine fingerprint to the sensor of
AFRS. Fingerprint Presentation Attack Detection (FPAD) is a countermeasure
intended to protect AFRS against fake or spoof fingerprints created using
various fabrication materials. In this paper, we have proposed a Convolutional
Neural Network (CNN) based technique that uses a Generative Adversarial Network
(GAN) to augment the dataset with spoof samples generated from the proposed
Open Patch Generator (OPG). This OPG is capable of generating realistic
fingerprint samples which have no resemblance to the existing spoof fingerprint
samples generated with other materials. The augmented dataset is fed to the
DenseNet classifier which helps in increasing the performance of the
Presentation Attack Detection (PAD) module for the various real-world attacks
possible with unknown spoof materials. Experimental evaluations of the proposed
approach are carried out on the Liveness Detection (LivDet) 2015, 2017, and
2019 competition databases. An overall accuracy of 96.20\%, 94.97\%, and
92.90\% has been achieved on the LivDet 2015, 2017, and 2019 databases,
respectively under the LivDet protocol scenarios. The performance of the
proposed PAD model is also validated in the cross-material and cross-sensor
attack paradigm which further exhibits its capability to be used under
real-world attack scenarios
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