FINGERPRINT PRESENTATION ATTACKS DETECTION IN THE DEEP LEARNING ERA: A “LIVDET” STORY
Attendance is free of charge but registration is required. Registered participants will receive dial-in credentials in the morning of the event.
Speaker: Gian Luca Marcialis, Univerity of Cagliari
More than 15 years ago, the research community claimed that fingerprints are very difficult to reproduce and steal. We lost our “innocence” when, between 2001 and 2002, some scholars fabricated artificial replicas of the fingers, named “fake fingers” or “gummy fingers”, that, if put on the fingerprint sensor surface, provided images impossible to distinguish from those of the live fingers, even by visual inspection of experts.
Since the fundamental problem was the lack of data, the organization of the International Fingerprint Liveness Detection Competition, known as “LivDet”, helped our research group to acquire strong know-how on the difficulties in fabricating fake fingers.
From 2015 to the 2019 edition, deep-learning-based algorithms outnumbered the ones based on handcraft features. In this webinar, we firstly review the main techniques for fingerprint presentation attack detection. We also summarize the LivDet experience. Finally, we provide some anticipations of what we expect by the incoming Livdet 2021. Our goal is to answer the following question: what did we gain, and what did we lose on moving from methods based on handcrafted features to deep learning ones? What can a possible pathway for the future of this topic be?
Gian Luca Marcialis is Associate Professor of Computer Engineering at the University of Cagliari, Italy. He obtained the MS degree in Electronic Engineering in 2000, and the Ph.D. degree in Computer Engineering in 2004. Since 2000 he joined the Pattern Recognition and Applications Laboratory (PRA Lab) of the Dept. of Electrical and Electronic Engineering – University of Cagliari, where he is currently director of the Lab’s Biometric Unit.
Gian Luca has published more than one hundred papers on international journals, conferences and books (H-index is 34 according to Google Scholar). His main contributions are on the multi-modal fusion of classifiers for ﬁngerprint classiﬁcation and veriﬁcation, the vulnerability assessment of multiple biometric systems, face recognition, adaptive biometric systems and ﬁngerprint liveness detection, as well as EEG signal processing.
He is involved in international and national research projects as responsible and team leader (“BullyBuster”, “Let’s Crowd”, “Tabula Rasa”) and is also Chair of the International Competition of Fingerprint Liveness Detection (LivDet).