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

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 Fuhr, David Menotti, Alexander Unnervik, Anjith George, Christophe Ecabert, Hatef Otroshi Shahreza, Parsa Rahimi, Sebastien Marcel, Ioannis Sarridis, Christos Koutlis, Georgia Baltsou, Symeon Papadopoulos, Christos Diou, Nicolo Di Domenico, Guido Borghi, Lorenzo Pellegrini, Enrique Mas-Candela, Angela Sanchez-Perez, Andrea Atzori, Fadi Boutros, Naser Damer, Gianni Fenu, Mirko Marras


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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 FRCSynonGoing, together with the proposed public ongoing benchmark, contribute significantly to the application of synthetic data to improve face recognition technology.
FRCSyn-onGoing,Face recognition,Generative AI,Demographic bias,Benchmark
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