FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data

• URL: http://arxiv.org/abs/2311.10476v1
• Date: Fri, 17 Nov 2023 12:15:40 GMT
• Title: FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
• Authors: Pietro Melzi and Ruben Tolosana and Ruben Vera-Rodriguez and Minchul Kim and Christian Rathgeb and Xiaoming Liu and Ivan DeAndres-Tame and Aythami Morales and Julian Fierrez and Javier Ortega-Garcia and Weisong Zhao and Xiangyu Zhu and Zheyu Yan and Xiao-Yu Zhang and Jinlin Wu and Zhen Lei and Suvidha Tripathi and Mahak Kothari and Md Haider Zama and Debayan Deb and Bernardo Biesseck and Pedro Vidal and Roger Granada and Guilherme Fickel and Gustavo F\"uhr and David Menotti and Alexander Unnervik and Anjith George and Christophe Ecabert and Hatef Otroshi Shahreza and Parsa Rahimi and S\'ebastien Marcel and Ioannis Sarridis and Christos Koutlis and Georgia Baltsou and Symeon Papadopoulos and Christos Diou and Nicol\`o Di Domenico and Guido Borghi and Lorenzo Pellegrini and Enrique Mas-Candela and \'Angela S\'anchez-P\'erez and Andrea Atzori and Fadi Boutros and Naser Damer and Gianni Fenu and Mirko Marras
• Abstract summary: This paper offers an overview of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first international challenge aiming to explore the use of synthetic data in face recognition to address existing limitations in the technology.
• Score: 82.5767720132393
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 be covered in more detail. This paper offers an overview of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first international challenge aiming to explore the use of synthetic data in face recognition to address existing limitations in the technology. Specifically, the FRCSyn Challenge 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. The results achieved in the FRCSyn Challenge, together with the proposed benchmark, contribute significantly to the application of synthetic data to improve face recognition technology.

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