Gian Luca Marcialis

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“Liveness detection”, a technique used to determine the vitality of a submitted biometric, has been implemented in fingerprint scanners in recent years. The goal for the LivDet 2011 competition is to compare software-based fingerprint liveness detection methodologies (Part 1), as well as fingerprint systems which incorporate liveness detection capabilities(More)
A spoof or fake is a counterfeit biometric that is used in an attempt to circumvent a biometric sensor. Liveness detection distinguishes between live and fake biometric traits. Liveness detection is based on the principle that additional information can be garnered above and beyond the data procured by a standard verification system, and this additional(More)
Fingerprint liveness detection consists in verifying if an input fingerprint image, acquired by a fingerprint verification system, belongs to a genuine user or is an artificial replica. Although several hardwareand software-based approaches have been proposed so far, this issue still remains unsolved due to the very high difficulty in finding effective(More)
We present new fingerprint classification algorithms based on two machine learning approaches: support vector machines (SVM), and recursive neural networks (RNN). RNN are trained on a structured representation of the fingerprint image. They are also used to extract a set of distributed features of the fingerprint which can be integrated in the SVM. SVM are(More)
Fingerprint recognition systems are vulnerable to artificial spoof fingerprint attacks, like molds made of silicone, gelatin or Play-Doh. “Liveness detection”, which is to detect vitality information from the biometric signature itself, has been proposed to defeat these kinds of spoof attacks. The goal for the LivDet 2009 competition is to compare different(More)
Recent experiments, reported in the third edition of Fingerprint Liveness Detection competition (LivDet 2013), have clearly shown that fingerprint liveness detection is a very difficult and challenging task. Although the number of approaches is large, none of them can be claimed as able to detect liveness of fingerprint traits with an acceptable error rate.(More)
Performances of face recognition systems based on principal component analysis can degrade quickly when input images exhibit substantial variations, due for example to changes in illumination or pose, compared to the templates collected during the enrolment stage. On the other hand, a lot of new unlabelled face images, which could be potentially used to(More)
Although face verification systems have proven to be reliable in ideal environments, they can be very sensitive to real environmental conditions. The system robustness can be increased by the fusion of different face verification algorithms. To the best of our knowledge, no face verification system tried exploiting the fusion of LDA and PCA. In our opinion,(More)
Performances of biometric recognition systems can degrade quickly when the input biometric traits exhibit substantial variations compared to the templates collected during the enrolment stage of system’s users. On the other hand, a lot of new unlabelled biometric data, which could be exploited to adapt the system to input data variations, are made available(More)
Template representativeness is a crucial problem in biometrics. The biometric data is subject to on-going changes which make initial enrolled templates usually acquired in controlled environment, un-representative of the input biometric data. Thus resulting in performance degradation of the system. To deal with this issue, "adaptive" biometric systems based(More)