378 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
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 algorithmsâ 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
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 âuser specificâ 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
Multibiometric security in wireless communication systems
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and
WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition.
First is the enrolment phase by which the database of watermarked fingerprints with
memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel.
Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present oneâs fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user.
The following three steps then involve speaker recognition including the user
responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user.
In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint
image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and
sliding neighborhood) have been followed with further two steps for embedding, and
extracting the watermark into the enhanced fingerprint image utilising Discrete
Wavelet Transform (DWT).
In the speaker recognition stage, the limitations of this technique in wireless
communication have been addressed by sending voice feature (cepstral coefficients)
instead of raw sample. This scheme is to reap the advantages of reducing the
transmission time and dependency of the data on communication channel, together
with no loss of packet. Finally, the obtained results have verified the claims
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