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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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