DIDACI , Luca

The score-level fusion approaches for fingerprint verification have been widely investigated. However, this investigation has been performed by studying each approach independently from the others, thus using different acquisition sensors, matching algorithms, fusion rules, and data sets. Due to this strong variability, the literature is lack...

Performance of mono- and multi-modal biometric systems depends on the representativeness of enrolled templates. Unfortunately, error rate values estimated during the system design are subject to variations due to several aspects: intra-class variations arising on small-medium time-window, and ageing, which is the natural process involving any...

The use of personal identity verification systems with multi-modal biometrics has been proposed in order to increase the performance and robustness against environmental variations and fraudulent attacks. Usually multi-modal fusion of biometrics is performed in parallel at the score-level by combining the individual matching scores. This...

Template update in biometric recognition system is aimed to improve the representativeness of available templates in order to make them adaptive to the large intra-class variations characterizing biometrics (e.g. fingerprints and faces). Among others, semi-supervised approaches to template update have been recently proposed. Since the lack of...

Classification algorithms based on template matching are used in many applications (e.g., face recognition). Performances of template matching classifiers are obviously affected by the representativeness of available templates. In many real applications, such representativeness can substantially decrease over the time (e.g., due to "aging"...

Although the large number of MCS topics, serial fusion of multiple classifiers has been poorly investigated so far. In this paper, we propose a model which, starting from the performance of individual classifiers and the traditional hypothesis of decision independence given the class, is able to estimate the performance, in terms of error...

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...

In this paper, a face recognition system based on the fusion of two well-known appearance-based algorithms, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), is proposed. Fusion is performed at the decision-level, that is, the outputs of the individual face recognition algorithms are combined. Two main benefits...

Dynamic classifier selection (DCS) plays a strategic role in the field of multiple classifier systems (MCS). This paper proposes a study on the performances of DCS by Local Accuracy estimation (DCS-LA). To this end, upper bounds against which the performances can be evaluated are proposed. The experimental results on five datasets clearly show...

Multiple classifier systems have been originally proposed for supervised classification tasks, and few works have dealt with semi-supervised multiple classifiers. However, there are important pattern recognition applications, such as multi-sensor remote sensing and multi-modal biometrics, which demand semi-supervised multiple classifier systems...

The co-training algorithm can be applied if a dataset admits a representation into two different feature sets (two views). However, its optimality is proved only under the conditions a) sufficiency of each view, and b) conditional independence given the class. We address the case where condition a) doesn't hold, as often happens in concrete...

The term adaptive biometric systems refers to biometric recognition systems in which an algorithm aimed to follow variations of the clients appearance has been implemented. Among others, the self update algorithm is used when only one biometric is available, and is able to add to the clients gallery novel data collected during system operation,...

Co-training is a well known semi-supervised learning algorithm, in which two classifiers are trained on two different views (feature sets): the initially small training set is iteratively updated with unlabelled samples classified with high confidence by one of the two classifiers. In this paper we address an issue that has been overlooked so...

As the Internet spreads to each corner of the world, computers are exposed to miscellaneous intrusions from the World Wide Web. Thus, we need effective intrusion detection systems to protect our computers from the intrusions. Traditional instance-based learning methods can onlyb e used to detect known intrusions since these methods...

The security of computer networks plays a strategic role in modern computer systems. In order to enforce high protection levels against threats, a number of software tools have been currently developed. Intrusion Detection Systems aim at detecting intruders who elude "first line" protection. In this paper, a pattern recognition approach to...

Diversity is deemed a crucial concept in the field of multiple classifier systems, although no exact definition has been found so far. Existing diversity measures exhibit some issues, both from the theoretical viewpoint, and from the practical viewpoint of ensemble construction. We propose to address some of these issues through the derivation...

Adaptive biometric systems update clients' templates during operation to account for natural changes over time (e.g., aging of biometric templates). Recently, it has been shown that this update can be exploited by an attacker to compromise the clients' templates: by presenting a proper sequence of fake biometric traits to the sensor, the...

Adaptive biometric recognition systems have been proposed to deal with natural changes of the clients' biometric traits due to multiple factors, like aging. However, their adaptability to changes may be exploited by an attacker to compromise the stored templates, either to impersonate a specific client, or to deny access to him. In this paper...

Randomization-based techniques for classifier ensemble construction, like Bagging and Random Forests, are well known and widely used. They consist of independently training the ensemble members on random perturbations of the training data or random changes of the learning algorithm. We argue that randomization techniques can be defined also by...

In this article, we review previous work on biometric security under a recent framework proposed in the field of adversarial machine learning. This allows us to highlight novel insights on the security of biometric systems when operating in the presence of intelligent and adaptive attackers that manipulate data to compromise normal system...

We address one of the main open issues about the use of diversity in multiple classifier systems: the effectiveness of the explicit use of diversity measures for creation of classifier ensembles. So far, diver- sity measures have been mostly used for ensemble pruning, namely, for selecting a subset of classifiers out of an original, larger...

In this paper we describe the road mapping methodology we developed in the context of the CyberROAD EU FP7 project, whose aim is to develop a research roadmap for cybercrime and cyber terrorism. To this aim we built on state-of-the-art methodologies and available guidelines, including related projects, and adapted them to the peculiarities of...

We describe the roadmapping method developed in the context of the CyberROAD EU FP7 project, the aim of which was to develop a research roadmap for cybercrime and cyberterrorism. To achieve this aim we build on state-of-the-art methodologies and guidelines, as well as related projects, and adapt them to the specific characteristics of...