E’ coordinatore della Divisione Biometria del Pattern Recognition and Applications Laboratory (PRA Lab) diretto dal Prof. Fabio Roli. L’attività di ricerca è incentrata sulle tecnologie biometriche per la sicurezza informatica. In particolare si occupa di classificazione e verifica di impronte digitali e volti, rilevazione di contraffazioni e sistemi multimodali. Ha al suo attivo oltre ottanta pubblicazioni fra riviste, atti di conferenze e congressi, capitoli di libro, tutte di impatto internazionale.
E’ revisore di progetti, riviste e conferenze internazionali.
E’ team leader e responsabile di progetti di ricerca internazionali pubblici (FP-European Union) e privati (Crossmatch) nonché progetti nazionali (PRIN, RAS) e locali (“Giovani Ricercatori”) e di collaborazione con il RaCIS di Cagliari.
He is team leader of the Biometric Unit of the Pattern Recognition and Applications Laboratory (PRA Lab) leaded by Prof. Fabio Roli. His research activity is focused on the biometric Technologies for information security. In particular, identification, verification and vulnerability analysis of fingerprint and face, multi-modal biometric systems. He has co-authored more than one hundred of publications in journal, conference proceedings and books chapters. He also co-authored the voice “Antispoofing: Multimodal” in the last edition of Encyclopedia of Biometrics.
He acts as referee for international projects, journals and conferences.
He is in charge of national and international research projects.
|Titolo:||An EEG-based biometric system using eigenvector centrality in resting state brain networks|
|Data di pubblicazione:||2015|
|Abstract:||Recently, there has been a growing interest in the use of brain activity for biometric systems. However, so far these studies have focused mainly on basic features of the Electroencephalography. In this study we propose an approach based on phase synchronization, to investigate personal distinctive brain network organization. To this end, the importance, in terms of centrality, of different regions was determined on the basis of EEG recordings. We hypothesized that nodal centrality enables the accurate identification of individuals. EEG signals from a cohort of 109 64-channels EEGs were band-pass filtered in the classical frequency bands and functional connectivity between the sensors was estimated using the Phase Lag Index. The resulting connectivity matrix was used to construct a weighted network, from which the nodal Eigenvector Centrality was computed. Nodal centrality was successively used as feature vector. Highest recognition rates were observed in the gamma band (equal error rate ( EER) = 0.044 ) and high beta band (EER= 0.102). Slightly lower recognition rate was observed in the low beta band (EER = 0.144), while poor recognition rates were observed for the others frequency bands. The reported results show that resting-state functional brain network topology provides better classification performance than using only a measure of functional connectivity, and may represent an optimal solution for the design of next generation EEG based biometric systems. This study also suggests that results from biometric systems based on high-frequency scalp EEG features should be interpreted with caution.|
|Tipologia:||1.1 Articolo in rivista|