FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems

• DOI: 10.1016/j.inffus.2024.102322
• Publication Date: 2024-03-05T16:46:41Z
• Authors: Pietro Melzi; Ruben Tolosana; Ruben Vera-Rodriguez; Minchul Kim; Christian Rathgeb; Xiaoming Liu; Ivan DeAndres-Tame; Aythami Morales; Julian Fierrez; Javier Ortega-Garcia; Weisong Zhao; Xiangyu Zhu; Zheyu Yan; Xiao-Yu Zhang; Jinlin Wu; Zhen Lei; Suvidha Tripathi; Mahak Kothari; Md Haider Zama; Debayan Deb; Bernardo Biesseck; Pedro Vidal; Roger Granada; Guilherme Fickel; Gustavo Führ; David Menotti; Alexander Unnervik; Anjith George; Christophe Ecabert; Hatef Otroshi Sha...; Parsa Rahimi; Sébastien Marcel; Ioannis Sarridis; Christos Koutlis; Georgia Baltsou; Symeon Papadopoulos; Christos Diou; Nicolò Di Domenico; Guido Borghi; Lorenzo Pellegrini; Enrique Mas-Candela; Ángela Sánchez-Pérez; Andrea Atzori; Fadi Boutros; Naser Damer; Gianni Fenu; Mirko Marras
• Keywords: Benchmarking; Benchmark (surveying); Sensor Fusion; Synthetic data; Fuse (electrical)

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