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

Parietal tACS at beta frequency improves vision in a crowding regime

Abstract Visual crowding is the inability to discriminate objects when presented with nearby flankers and sets a fundamental limit for conscious perception. Beta oscillations in the parietal cortex were found to be associated to crowding, with higher beta amplitude related to better crowding resilience. An open question is whether beta activity directly and selectively modulates crowding. We employed transcranial alternating current stimulation (tACS) in the beta band (18-Hz), in the alpha band (10-Hz) or in a sham regime, asking whether 18-Hz tACS would selectively improve the perception of crowded stimuli by increasing parietal beta activity. Resting electroencephalography (EEG) was measured before and after stimulation to test the influence of tACS on endogenous oscillations. Consistently with our predictions, we found that 18-Hz tACS, as compared to 10-Hz tACS and sham stimulation, reduced crowding. This improvement was found specifically in the contralateral visual hemifield and was accompanied by an increased amplitude of EEG beta oscillations, confirming an effect on endogenous brain rhythms. These results support a causal relationship between parietal beta oscillations and visual crowding and provide new insights into the precise oscillatory mechanisms involved in human vision

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

LivDet in Action - Fingerprint Liveness Detection Competition 2019

The International Fingerprint liveness Detection Competition (LivDet) is an open and well-acknowledged meeting point of academies and private companies that deal with the problem of distinguishing images coming from reproductions of fingerprints made of artificial materials and images relative to real fingerprints. In this edition of LivDet we invited the competitors to propose integrated algorithms with matching systems. The goal was to investigate at which extent this integration impact on the whole performance. Twelve algorithms were submitted to the competition, eight of which worked on integrated systems.Comment: Preprint version of a paper accepted at ICB 201

LivDet 2017 Fingerprint Liveness Detection Competition 2017

Fingerprint Presentation Attack Detection (FPAD) deals with distinguishing images coming from artificial replicas of the fingerprint characteristic, made up of materials like silicone, gelatine or latex, and images coming from alive fingerprints. Images are captured by modern scanners, typically relying on solid-state or optical technologies. Since from 2009, the Fingerprint Liveness Detection Competition (LivDet) aims to assess the performance of the state-of-the-art algorithms according to a rigorous experimental protocol and, at the same time, a simple overview of the basic achievements. The competition is open to all academics research centers and all companies that work in this field. The positive, increasing trend of the participants number, which supports the success of this initiative, is confirmed even this year: 17 algorithms were submitted to the competition, with a larger involvement of companies and academies. This means that the topic is relevant for both sides, and points out that a lot of work must be done in terms of fundamental and applied research.Comment: presented at ICB 201

On the interoperability of capture devices in fingerprint presentation attacks detection

Abstract A presentation attack consists in submitting to the fingerprint capture device an artificial replica of the finger of the targeted client. If the sensor is not equipped with an appropriate algorithm aimed to detect the fingerprint spoof, the system processes the obtained image as a one belonging to a real fingerprint. In order to face this problem, several presentation attacks detection (PAD) algorithms have been proposed so far. Current methods heavily rely on features extracted from a large data set of fake and real fingerprint images, and an appropriate classifier trained with such data to distinguish between live (real) and fake (spoof) fingerprint images. Building such data set requires a significant effort for fabricating samples of fake fingerprints, with the most effective materials used to circumvent the sensor. Interesting and promising results have been obtained, but they also suggest that the PAD is tailored on the particular sensor. Small and significant differences also occur when a novel version of the same sensor is released, and this may affect the PAD. Therefore, making a PAD interoperable is among the main current issues when considering fingerprints as the first level of protection and security of logical or physical resources. This paper is a first attempt to assess at which extent the sensor interoperability can be an issue for fingerprint PADs and to eventually propose a solution to this limitation. In particular, textural features will be under focus and a feature space transformation method based on the least square is proposed

Distributed ledger technologies for peer-to-peer local markets in distribution networks

The newest Distributed Ledger Technology platforms, which delegate the execution of complex tasks in the form of Smart Contracts, make it possible to devise novel local electricity market frameworks, which are performed in a fully automated fashion. This paper proposes a novel fully automated platform for energy and ancillary service markets in distribution networks, able to run in a decentralized fashion, bypassing the need for a physical central authority. The proposed platform, able to perform the role of Virtual Decentralized Market Authority, shows excellent potential applications in the management of local ancillary service markets in local energy communities of various sizes. The proposed Virtual Decentralized Market Authority showed reasonable running costs and comparable technical management capabilities with respect to a physical, centralized managing authority

Clinical global impression-severity score as a reliable measure for routine evaluation of remission in schizophrenia and schizoaffective disorders

Aims: This study aimed to compare the performance of Positive and Negative Syndrome Scale (PANSS) symptom severity criteria established by the Remission in Schizophrenia Working Group (RSWG) with criteria based on Clinical Global Impression (CGI) severity score. The 6-month duration criterion was not taken into consideration. Methods: A convenience sample of 112 chronic psychotic outpatients was examined. Symptomatic remission was evaluated according to RSWG severity criterion and to a severity criterion indicated by the overall score obtained at CGI-Schizophrenia (CGI-SCH) rating scale (≤3) (CGI-S). Results: Clinical remission rates of 50% and 49.1%, respectively, were given by RSWG and CGI-S, with a significant level of agreement between the two criteria in identifying remitted and non-remitted cases. Mean scores at CGI-SCH and PANSS scales were significantly higher among remitters, independent of the remission criteria adopted. Measures of cognitive functioning were largely independent of clinical remission evaluated according to both RSWG and CGI-S. When applying RSWG and CGI-S criteria, the rates of overall good functioning yielded by Personal and Social Performance scale (PSP) were 32.1% and 32.7%, respectively, while the mean scores at PSP scale differed significantly between remitted and non-remitted patients, independent of criteria adopted. The proportion of patients judged to be in a state of well-being on Social Well-Being Under Neuroleptics-Short Version scale (SWN-K) were, respectively, 66.1% and 74.5% among remitters according to RSWG and CGI-S; the mean scores at the SWN scale were significantly higher only among remitters according to CGI-S criteria. Conclusions: CGI severity criteria may represent a valid and user-friendly alternative for use in identifying patients in remission, particularly in routine clinical practic

Association of kidney disease measures with risk of renal function worsening in patients with type 1 diabetes

Background: Albuminuria has been classically considered a marker of kidney damage progression in diabetic patients and it is routinely assessed to monitor kidney function. However, the role of a mild GFR reduction on the development of stage 653 CKD has been less explored in type 1 diabetes mellitus (T1DM) patients. Aim of the present study was to evaluate the prognostic role of kidney disease measures, namely albuminuria and reduced GFR, on the development of stage 653 CKD in a large cohort of patients affected by T1DM. Methods: A total of 4284 patients affected by T1DM followed-up at 76 diabetes centers participating to the Italian Association of Clinical Diabetologists (Associazione Medici Diabetologi, AMD) initiative constitutes the study population. Urinary albumin excretion (ACR) and estimated GFR (eGFR) were retrieved and analyzed. The incidence of stage 653 CKD (eGFR < 60 mL/min/1.73 m2) or eGFR reduction > 30% from baseline was evaluated. Results: The mean estimated GFR was 98 \ub1 17 mL/min/1.73m2 and the proportion of patients with albuminuria was 15.3% (n = 654) at baseline. About 8% (n = 337) of patients developed one of the two renal endpoints during the 4-year follow-up period. Age, albuminuria (micro or macro) and baseline eGFR < 90 ml/min/m2 were independent risk factors for stage 653 CKD and renal function worsening. When compared to patients with eGFR > 90 ml/min/1.73m2 and normoalbuminuria, those with albuminuria at baseline had a 1.69 greater risk of reaching stage 3 CKD, while patients with mild eGFR reduction (i.e. eGFR between 90 and 60 mL/min/1.73 m2) show a 3.81 greater risk that rose to 8.24 for those patients with albuminuria and mild eGFR reduction at baseline. Conclusions: Albuminuria and eGFR reduction represent independent risk factors for incident stage 653 CKD in T1DM patients. The simultaneous occurrence of reduced eGFR and albuminuria have a synergistic effect on renal function worsening

Experimental Results on the Feature-level Fusion of Multiple Fingerprint Liveness Detection Algorithms

The aim of fingerprint liveness detection is to detect if a fingerprint image, sensed by an electronic device, belongs to an alive fingertip or to an artificial replica of it. It is well-known that a fingerprint can be replicated and standard electronic sensors cannot distinguish between a replica and an alive fingerprint image. Accordingly, several countermeasures in terms of fingerprint liveness detection algorithms have been proposed, but their performance is not yet acceptable. However, no works studied the possibility of combining different feature sets, thus exploiting the eventual complementarity among them. In this paper, we show some preliminary experiments on feature-level fusion of several algorithms, including a novel feature set proposed by the authors. Experiments are carried out on the datasets available at Second International Fingerprint Liveness Detection Competition (LivDet 2011). Reported results clearly show that multiple feature sets allow improving the liveness detection performance