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:||Analysis of unsupervised template update in biometric recognition systems|
|Data di pubblicazione:||2014|
|Citazione:||Analysis of unsupervised template update in biometric recognition systems / Didaci L; Marcialis G; Roli F. - 37:1(2014), pp. 151-160.|
|Abstract:||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 biometrics. This causes the increase of the False Rejection Rate (genuine users are no more recognized) or the False Acceptance Rate (impostors are misclassified as genuine users), or both. In fact, several vendors strongly suggest to repeat enrolment sessions in order to collect, over time, a set of templates representative enough. As alternative, automatic template update algorithms, which exploit the own-knowledge of the mono- or multi-modal biometric system, on a batch of samples collected during system operations without the human supervision, have been proposed. Preliminary experimental results have shown that these algorithms are promising, but the motivation of their behaviour has not yet been explained. This paper is aimed to fill such gap, by showing that behaviour of self- and co-update may be explained by exploiting the concept of path-based clustering. Therefore, problems as 'intra-class' variations and ageing are dependent on the path-based cluster followed by each algorithm. Moreover, we show that the performance of co-update is superior than that of self-update, by a simulative model. The path-based clustering theory applied to self- and co-update algorithms, as well as the proposed model, are experimentally validated on the large DIEE Multimodal data set, the only one publicly available and explicitly conceived for comparing template update algorithms. © 2013 Elsevier Ltd. All rights reserved.|
|Tipologia:||1.1 Articolo in rivista|