activity
20182022
most citedLivDet 2021 Fingerprint Liveness Detection Competition -- Into the unknown

28 citations · 66 across the 4 of their papers we have counts for

collaborators

11 papers

cs.CV20221 cited

Review of the Fingerprint Liveness Detection (LivDet) competition series: from 2009 to 2021

Marco Micheletto, Giulia Orrù, Roberto Casula +3

Fingerprint authentication systems are highly vulnerable to artificial reproductions of fingerprint, called fingerprint presentation attacks. Detecting presentation attacks is not…

cs.CR202110 cited

Fingerprint recognition with embedded presentation attacks detection: are we ready?

Marco Micheletto, Gian Luca Marcialis, Giulia Orrù +1

The diffusion of fingerprint verification systems for security applications makes it urgent to investigate the embedding of software-based presentation attack detection algorithms…

cs.CV202128 cited

LivDet 2021 Fingerprint Liveness Detection Competition -- Into the unknown

Roberto Casula, Marco Micheletto, Giulia Orrù +4

The International Fingerprint Liveness Detection Competition is an international biennial competition open to academia and industry with the aim to assess and report advances in Fi…

cs.CV2020

Electroencephalography signal processing based on textural features for monitoring the driver's state by a Brain-Computer Interface

Giulia Orrù, Marco Micheletto, Fabio Terranova +1

In this study we investigate a textural processing method of electroencephalography (EEG) signal as an indicator to estimate the driver's vigilance in a hypothetical Brain-Computer…

cs.CV2020

Detecting Anomalies from Video-Sequences: a Novel Descriptor

Giulia Orrù, Davide Ghiani, Maura Pintor +2

We present a novel descriptor for crowd behavior analysis and anomaly detection. The goal is to measure by appropriate patterns the speed of formation and disintegration of groups…

cs.CV2020

Are Adaptive Face Recognition Systems still Necessary? Experiments on the APE Dataset

Giulia Orrù, Marco Micheletto, Julian Fierrez +1

In the last five years, deep learning methods, in particular CNN, have attracted considerable attention in the field of face-based recognition, achieving impressive results. Despit…