199 research outputs found
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
Safeguarding Biometric Authentication Systems from Fingerprint Spoof Attacks
This publication describes processes, methods, and techniques of safeguarding biometric authentication systems from fingerprint spoof attacks. A fingerprint spoof attack occurs when a malicious party attempts to access a user’s computing device by mimicking/replicating biometric identifiers inherent to the user’s fingerprint. To prevent fingerprint spoof attacks, an improved biometric authentication protocol includes, in a first step, verifying a user’s identity and, in a second step, determining if the finger presented is alive. As a result, fingerprint spoof attacks can be rejected when it is determined that the spoof finger does not exhibit characteristics of life
Biometrics in ABC: counter-spoofing research
Automated border control (ABC) is concerned with fast and secure processing for intelligence-led identification. The
FastPass project aims to build a harmonised, modular reference system for future European ABC. When biometrics is taken on
board as identity, spoofing attacks become a concern. This paper presents current research in algorithm development for
counter-spoofing attacks in biometrics. Focussing on three biometric traits, face, fingerprint, and iris, it examines possible types
of spoofing attacks, and reviews existing algorithms reported in relevant academic papers in the area of countering measures to
biometric spoofing attacks. It indicates that the new developing trend is fusion of multiple biometrics against spoofing attacks
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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