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

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 ; Di Domenico, Nicolò ; Borghi, Guido ; Pellegrini, Lorenzo ; Mas-Candela, Enrique ; Sánchez-Pérez, Ángela ; Atzori, Andrea ; Fenu, Gianni ; Boutros, Fadi ; Marras, Mirko ; Damer, Naser
ed.: Institute of Electrical and Electronics Engineers (2024)
FRCSyn Challenge at WACV 2024: Face Recognition Challenge in the Era of Synthetic Data.
Winter Conference on Applications of Computer Vision 2024. Waikoloa, Hawaii, USA (01.01. - 06.01.2024)
doi: 10.1109/WACVW60836.2024.00100
Conference or Workshop Item, Bibliographie

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.

Item Type: Conference or Workshop Item
Erschienen: 2024
Creators: 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 ; Di Domenico, Nicolò ; Borghi, Guido ; Pellegrini, Lorenzo ; Mas-Candela, Enrique ; Sánchez-Pérez, Ángela ; Atzori, Andrea ; Fenu, Gianni ; Boutros, Fadi ; Marras, Mirko ; Damer, Naser
Type of entry: Bibliographie
Title: FRCSyn Challenge at WACV 2024: Face Recognition Challenge in the Era of Synthetic Data
Language: English
Date: 2024
Place of Publication: Piscataway, NJ
Publisher: IEEE
Book Title: 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
Event Title: Winter Conference on Applications of Computer Vision 2024
Event Location: Waikoloa, Hawaii, USA
Event Dates: 01.01. - 06.01.2024
DOI: 10.1109/WACVW60836.2024.00100
Corresponding Links:
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.

Uncontrolled Keywords: Biometrics, Face recognition, Image generation, Machine learning, Deep learning
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Date Deposited: 17 May 2024 08:30
Last Modified: 03 Jun 2024 06:33
PPN: 518772853
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