53 citations · 98 across the 11 of their papers we have counts for
10 papers · 1 filter
EERLoss: A Novel Loss Function for Training Deep Biometric Models. A Case Study in Keystroke Dynamics
Nahuel Gonzalez, Marta Robledo-Moreno, Ivan DeAndres-Tame +2
Deep learning approaches to biometric verification are commonly trained by optimizing indirect objectives, creating a misalignment between the optimization process and the primary…
Is Visual Realism Enough? Evaluating Gait Biometric Fidelity in Generative AI Human Animation
Ivan DeAndres-Tame, Chengwei Ye, Ruben Tolosana +2
Generative AI (GenAI) models have revolutionized animation, enabling the synthesis of humans and motion patterns with remarkable visual fidelity. However, generating truly realisti…
AG-VPReID 2025: Aerial-Ground Video-based Person Re-identification Challenge Results
Kien Nguyen, Clinton Fookes, Sridha Sridharan +20
Person re-identification (ReID) across aerial and ground vantage points has become crucial for large-scale surveillance and public safety applications. Although significant progres…
Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data
Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi +56
Synthetic data is gaining increasing popularity for face recognition technologies, mainly due to the privacy concerns and challenges associated with obtaining real data, including…
From Pixels to Words: Leveraging Explainability in Face Recognition through Interactive Natural Language Processing
Ivan DeAndres-Tame, Muhammad Faisal, Ruben Tolosana +3
Face Recognition (FR) has advanced significantly with the development of deep learning, achieving high accuracy in several applications. However, the lack of interpretability of th…
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi +55
Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class v…
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…
How Good is ChatGPT at Face Biometrics? A First Look into Recognition, Soft Biometrics, and Explainability
Ivan DeAndres-Tame, Ruben Tolosana, Ruben Vera-Rodriguez +3
Large Language Models (LLMs) such as GPT developed by OpenAI, have already shown astonishing results, introducing quick changes in our society. This has been intensified by the rel…
IEEE BigData 2023 Keystroke Verification Challenge (KVC)
Giuseppe Stragapede, Ruben Vera-Rodriguez, Ruben Tolosana +26
This paper describes the results of the IEEE BigData 2023 Keystroke Verification Challenge (KVC), that considers the biometric verification performance of Keystroke Dynamics (KD),…
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Pietro Melzi, Ruben Tolosana, Ruben Vera-Rodriguez +44
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must…