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20162026
most citedSDFR: Synthetic Data for Face Recognition Competition

7 citations · 18 across the 25 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.CV2024★ 1 cited

Depth-based Privileged Information for Boosting 3D Human Pose Estimation on RGB

Alessandro Simoni, Francesco Marchetti, Guido Borghi +6

Despite the recent advances in computer vision research, estimating the 3D human pose from single RGB images remains a challenging task, as multiple 3D poses can correspond to the…

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

Adversarial Identity Injection for Semantic Face Image Synthesis

Giuseppe Tarollo, Tomaso Fontanini, Claudio Ferrari +2

Nowadays, deep learning models have reached incredible performance in the task of image generation. Plenty of literature works address the task of face generation and editing, with…

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…