Summary of Second Edition Frcsyn Challenge at Cvpr 2024: Face Recognition Challenge in the Era Of Synthetic Data, by Ivan Deandres-tame et al.
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
by Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi, Ruben Vera-Rodriguez, Minchul Kim, Christian Rathgeb, Xiaoming Liu, Aythami Morales, Julian Fierrez, Javier Ortega-Garcia, Zhizhou Zhong, Yuge Huang, Yuxi Mi, Shouhong Ding, Shuigeng Zhou, Shuai He, Lingzhi Fu, Heng Cong, Rongyu Zhang, Zhihong Xiao, Evgeny Smirnov, Anton Pimenov, Aleksei Grigorev, Denis Timoshenko, Kaleb Mesfin Asfaw, Cheng Yaw Low, Hao Liu, Chuyi Wang, Qing Zuo, Zhixiang He, Hatef Otroshi Shahreza, Anjith George, Alexander Unnervik, Parsa Rahimi, Sébastien Marcel, Pedro C. Neto, Marco Huber, Jan Niklas Kolf, Naser Damer, Fadi Boutros, Jaime S. Cardoso, Ana F. Sequeira, Andrea Atzori, Gianni Fenu, Mirko Marras, Vitomir Štruc, Jiang Yu, Zhangjie Li, Jichun Li, Weisong Zhao, Zhen Lei, Xiangyu Zhu, Xiao-Yu Zhang, Bernardo Biesseck, Pedro Vidal, Luiz Coelho, Roger Granada, David Menotti
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an overview of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at CVPR 2024. The challenge aims to investigate the use of synthetic data in face recognition, addressing limitations such as data privacy concerns, demographic biases, and performance constraints in challenging situations like aging, pose variations, and occlusions. Unlike the previous edition, this one introduces new sub-tasks that allow participants to explore novel face generative methods. The paper proposes an experimental protocol and benchmarking, contributing significantly to the application of synthetic data to face recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how we can use fake faces in training machine learning models for recognizing real faces. This is important because there are not enough real faces to train models on, or some faces might have privacy concerns. The challenge wants to see if we can make machines better at recognizing faces using made-up faces. There’s a new part of the challenge that lets people try out different ways to create fake faces. |
Keywords
» Artificial intelligence » Face recognition » Machine learning » Synthetic data
