FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems
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Published:2024-07
Issue:
Volume:107
Page:102322
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ISSN:1566-2535
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Container-title:Information Fusion
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language:en
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Short-container-title:Information Fusion
Author:
Melzi PietroORCID, Tolosana RubenORCID, 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
Funder
Gobierno de España Ministerio de Ciencia, Innovación y Universidades European Union Ministerio de Asuntos Economicos y Transformacion Digital
Reference89 articles.
1. Biometrics recognition using deep learning: A survey;Minaee;Artif. Intell. Rev.,2023 2. J. Deng, J. Guo, N. Xue, S. Zafeiriou, ArcFace: Additive angular margin loss for deep face recognition, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. 3. M. Kim, A.K. Jain, X. Liu, AdaFace: Quality Adaptive Margin for Face Recognition, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 4. Deep face recognition: A survey;Wang;Neurocomputing,2021 5. G.B. Huang, M. Mattar, T. Berg, E. Learned-Miller, Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments, in: Proceedings of the Workshop on Faces in ‘Real-Life’ Images: Detection, Alignment, and Recognition, 2008.
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