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:||Adaptive Classification for Person Re-Identification Driven by Change Detection|
|Data di pubblicazione:||2014|
|Abstract:||Person re-identification from facial captures remains a challenging problem in video surveillance, in large part due to variations in capture conditions over time. The facial model of a target individual is typically designed during an enrolment phase, using a limited number of reference samples, and may be adapted as new reference videos become available. However incremental learning of classifiers in changing capture conditions may lead to knowledge corruption. This paper presents an active framework for an adaptive multi-classifier system for video-to-video face recognition in changing surveillance environments. To estimate a facial model during the enrolment of an individual, facial captures extracted from a reference video are employed to train an individual-specific incremental classifier. To sustain a high level of performance over time, a facial model is adapted in response to new reference videos according the type of concept change. If the system detects that the facial captures of an individual incorporate a gradual pattern of change, the corresponding classifier(s) are adapted through incremental learning. In contrast, to avoid knowledge corruption, if an abrupt pattern of change is detected, a new classifier is trained on the new video data, and combined with the individual’s previously-trained classifiers. For validation, a specific implementation is proposed, with ARTMAP classifiers updated using an incremental learning strategy based on Particle Swarm Optimization, and the Hellinger Drift Detection Method is used for change detection. Simulation results produced with Faces in Action video data indicate that the proposed system allows for scalable architectures that maintains a significantly higher level of accuracy over time than a reference passive system and an adaptive Transduction Confidence Machine-kNN classifier, while controlling computational complexity|
|Tipologia:||4.1 Contributo in Atti di convegno|