Isabelle Hupont Torres (<i>she/her</i>)

Isabelle Hupont Torres (she/her)

Researcher on Trustworthy AI and Virtual Worlds

Publications


You can check all the articles I have co-authored in my public Google Scholar profile. Below, I present a selection of them, either because they have some associated material (e.g. a dataset, a repository) or because of their impact on Trustworthy AI and Virtual Worlds.

Next generation virtual worlds: societal, technological, economic and policy challenges for the EU

Published in Publications Office of the European Union, 2023

This scientific report provides a multidisciplinary and multisectoral perspective on next generation virtual worlds, the opportunities they offer, the challenges they might bring and a techno-economic analysis of current key players.

Citation: Hupont Torres, I., Charisi, V., De Prato, G., Pogorzelska, K., Schade, S., Kotsev, A., Sobolewski, M., Duch Brown, N., Calza, E., Dunker, C., Di Girolamo, F., Bellia, M., Hledik, J., Nai Fovino, I. and Vespe, M., Next Generation Virtual Worlds: Societal, Technological, Economic and Policy Challenges for the EU, Publications Office of the European Union, Luxembourg, 2023, doi:10.2760/51579, JRC133757. https://publications.jrc.ec.europa.eu/repository/handle/JRC133757

The landscape of facial processing applications in the context of the European AI Act and the development of trustworthy systems

Published in Nature Scientific Reports, 2022

This article presents the landscape of facial processing tasks, systems and applications. It identifies the 60 most relevant applications adopted for real-world uses, which are analysed under the lens of the European AI Act proposal and the 7 requirements for Trustworthy AI defined by the European High Level Expert Group on AI. It also reflects on current research, technical and societal challenges towards trustworthy facial processing systems.

Citation: Hupont Torres, I., Tolan, S., Gunes, H. and Gomez Gutierrez, E., The landscape of facial processing applications in the context of the European AI Act and the development of trustworthy systems, Nature Scientific Reports, ISSN 2045-2322, 12(1), 2022. https://www.nature.com/articles/s41598-022-14981-6

DemogPairs: quantifying the impact of demographic imbalance in deep face recognition

Published in IEEE Int. Conf. on Automatic Face and Gesture Recognition (FG 2019), 2019

In this paper we present DemogPairs, a publicly released validation dataset with 10.8K facial images and 58.3M identity verification pairs, distributed in demographically-balanced folds of Asian, Black and White females and males. DemogPairs and its associated benchmarking protocol are conceived to explore demographic biases and the cross-demographic behaviour of face recognition algorithms. More information on how to obtain DemogPairs here.

Citation: Hupont, I. and Fernández, C., DemogPairs: quantifying the impact of demographic imbalance in deep face recognition, 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019). IEEE, 2019. https://ieeexplore.ieee.org/abstract/document/8756625