activity
20232026
most citedSDFR: Synthetic Data for Face Recognition Competition

7 citations · 10 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2026

Arc2Morph: Identity-Preserving Facial Morphing with Arc2Face

Nicolò Di Domenico, Annalisa Franco, Matteo Ferrara +1

Face morphing attacks are widely recognized as one of the most challenging threats to face recognition systems used in electronic identity documents. These attacks exploit a critic…

cs.CV2024

ONOT: a High-Quality ICAO-compliant Synthetic Mugshot Dataset

Nicolò Di Domenico, Guido Borghi, Annalisa Franco +1

Nowadays, state-of-the-art AI-based generative models represent a viable solution to overcome privacy issues and biases in the collection of datasets containing personal informatio…

cs.CV2024★ 2 cited

Dealing with Subject Similarity in Differential Morphing Attack Detection

Nicolò Di Domenico, Guido Borghi, Annalisa Franco +1

The advent of morphing attacks has posed significant security concerns for automated Face Recognition systems, raising the pressing need for robust and effective Morphing Attack De…

cs.CV2024

V-MAD: Video-based Morphing Attack Detection in Operational Scenarios

Guido Borghi, Annalisa Franco, Nicolò Di Domenico +2

In response to the rising threat of the face morphing attack, this paper introduces and explores the potential of Video-based Morphing Attack Detection (V-MAD) systems in real-worl…

cs.CV2024★ 7 cited

SDFR: Synthetic Data for Face Recognition Competition

Hatef Otroshi Shahreza, Christophe Ecabert, Anjith George +25

Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advance…

cs.LG2024

Enabling On-device Continual Learning with Binary Neural Networks

Lorenzo Vorabbi, Davide Maltoni, Guido Borghi +1

On-device learning remains a formidable challenge, especially when dealing with resource-constrained devices that have limited computational capabilities. This challenge is primari…

cs.LG2023★ 1 cited

Optimizing data-flow in Binary Neural Networks

L. Vorabbi, D. Maltoni, S. Santi

Binary Neural Networks (BNNs) can significantly accelerate the inference time of a neural network by replacing its expensive floating-point arithmetic with bitwise operations. Most…