Melzi, Pietro ; Tolosana, Ruben ; Vera-Rodriguez, Ruben ; Kim, Minchul ; Rathgeb, Christian ; Liu, Xiaoming ; DeAndres-Tame, Ivan ; Morales, Aythami ; Fierrez, Julian ; Ortega-Garcia, Javier ; Zhao, Weisong ; Zhu, Xiangyu ; Yan, Zheyu ; Zhang, Xiao-Yu ; Wu, Jinlin ; Lei, Zhen ; Tripathi, Suvidha ; Kothari, Mahak ; Zama, Md Haider ; Deb, Debayan ; Biesseck, Bernardo ; Vidal, Pedro ; Granada, Roger ; Fickel, Guilherme ; Führ, Gustavo ; Menotti, David ; Unnervik, Alexander ; George, Anjith ; Ecabert, Christophe ; Shahreza, Hatef Otroshi ; Rahimi, Parsa ; Marcel, Sébastien ; Sarridis, Ioannis ; Koutlis, Christos ; Baltsou, Georgia ; Papadopoulos, Symeon ; Diou, Christos ; Domenico, Nicolò Di ; Borghi, Guido ; Pellegrini, Lorenzo ; Mas-Candela, Enrique ; Sánchez-Pérez, Ángela ; Atzori, Andrea ; Boutros, Fadi ; Damer, Naser ; Fenu, Gianni ; Marras, Mirko (2024)
FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems.
In: Information Fusion, 107
doi: 10.1016/j.inffus.2024.102322
Artikel, Bibliographie

Kurzbeschreibung (Abstract)

This article presents FRCSyn-onGoing, an ongoing challenge for face recognition where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases and standard experimental protocols. FRCSyn-onGoing is based on the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first face recognition international challenge aiming to explore the use of real and synthetic data independently, and also their fusion, in order to address existing limitations in the technology. Specifically, FRCSyn-onGoing targets concerns related to data privacy issues, demographic biases, generalization to unseen scenarios, and performance limitations in challenging scenarios, including significant age disparities between enrollment and testing, pose variations, and occlusions. To enhance face recognition performance, FRCSyn-onGoing strongly advocates for information fusion at various levels, starting from the input data, where a mix of real and synthetic domains is proposed for specific tasks of the challenge. Additionally, participating teams are allowed to fuse diverse networks within their proposed systems to improve the performance. In this article, we provide a comprehensive evaluation of the face recognition systems and results achieved so far in FRCSyn-onGoing. The results obtained in FRCSyn-onGoing, together with the proposed public ongoing benchmark, contribute significantly to the application of synthetic data to improve face recognition technology.

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