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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2404.10378 |
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| _version_ | 1866911310973763584 |
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| author | DeAndres-Tame, Ivan Tolosana, Ruben Melzi, Pietro Vera-Rodriguez, Ruben Kim, Minchul Rathgeb, Christian Liu, Xiaoming Morales, Aythami Fierrez, Julian Ortega-Garcia, Javier Zhong, Zhizhou Huang, Yuge Mi, Yuxi Ding, Shouhong Zhou, Shuigeng He, Shuai Fu, Lingzhi Cong, Heng Zhang, Rongyu Xiao, Zhihong Smirnov, Evgeny Pimenov, Anton Grigorev, Aleksei Timoshenko, Denis Asfaw, Kaleb Mesfin Low, Cheng Yaw Liu, Hao Wang, Chuyi Zuo, Qing He, Zhixiang Shahreza, Hatef Otroshi George, Anjith Unnervik, Alexander Rahimi, Parsa Marcel, Sébastien Neto, Pedro C. Huber, Marco Kolf, Jan Niklas Damer, Naser Boutros, Fadi Cardoso, Jaime S. Sequeira, Ana F. Atzori, Andrea Fenu, Gianni Marras, Mirko Štruc, Vitomir Yu, Jiang Li, Zhangjie Li, Jichun Zhao, Weisong Lei, Zhen Zhu, Xiangyu Zhang, Xiao-Yu Biesseck, Bernardo Vidal, Pedro Coelho, Luiz Granada, Roger Menotti, David |
| author_facet | DeAndres-Tame, Ivan Tolosana, Ruben Melzi, Pietro Vera-Rodriguez, Ruben Kim, Minchul Rathgeb, Christian Liu, Xiaoming Morales, Aythami Fierrez, Julian Ortega-Garcia, Javier Zhong, Zhizhou Huang, Yuge Mi, Yuxi Ding, Shouhong Zhou, Shuigeng He, Shuai Fu, Lingzhi Cong, Heng Zhang, Rongyu Xiao, Zhihong Smirnov, Evgeny Pimenov, Anton Grigorev, Aleksei Timoshenko, Denis Asfaw, Kaleb Mesfin Low, Cheng Yaw Liu, Hao Wang, Chuyi Zuo, Qing He, Zhixiang Shahreza, Hatef Otroshi George, Anjith Unnervik, Alexander Rahimi, Parsa Marcel, Sébastien Neto, Pedro C. Huber, Marco Kolf, Jan Niklas Damer, Naser Boutros, Fadi Cardoso, Jaime S. Sequeira, Ana F. Atzori, Andrea Fenu, Gianni Marras, Mirko Štruc, Vitomir Yu, Jiang Li, Zhangjie Li, Jichun Zhao, Weisong Lei, Zhen Zhu, Xiangyu Zhang, Xiao-Yu Biesseck, Bernardo Vidal, Pedro Coelho, Luiz Granada, Roger Menotti, David |
| contents | Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class variability, time and errors produced in manual labeling, and in some cases privacy concerns, among others. This paper presents an overview of the 2nd edition of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at CVPR 2024. FRCSyn aims to investigate the use of synthetic data in face recognition to address current technological limitations, including data privacy concerns, demographic biases, generalization to novel scenarios, and performance constraints in challenging situations such as aging, pose variations, and occlusions. Unlike the 1st edition, in which synthetic data from DCFace and GANDiffFace methods was only allowed to train face recognition systems, in this 2nd edition we propose new sub-tasks that allow participants to explore novel face generative methods. The outcomes of the 2nd FRCSyn Challenge, along with the proposed experimental protocol and benchmarking contribute significantly to the application of synthetic data to face recognition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10378 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data DeAndres-Tame, Ivan Tolosana, Ruben Melzi, Pietro Vera-Rodriguez, Ruben Kim, Minchul Rathgeb, Christian Liu, Xiaoming Morales, Aythami Fierrez, Julian Ortega-Garcia, Javier Zhong, Zhizhou Huang, Yuge Mi, Yuxi Ding, Shouhong Zhou, Shuigeng He, Shuai Fu, Lingzhi Cong, Heng Zhang, Rongyu Xiao, Zhihong Smirnov, Evgeny Pimenov, Anton Grigorev, Aleksei Timoshenko, Denis Asfaw, Kaleb Mesfin Low, Cheng Yaw Liu, Hao Wang, Chuyi Zuo, Qing He, Zhixiang Shahreza, Hatef Otroshi George, Anjith Unnervik, Alexander Rahimi, Parsa Marcel, Sébastien Neto, Pedro C. Huber, Marco Kolf, Jan Niklas Damer, Naser Boutros, Fadi Cardoso, Jaime S. Sequeira, Ana F. Atzori, Andrea Fenu, Gianni Marras, Mirko Štruc, Vitomir Yu, Jiang Li, Zhangjie Li, Jichun Zhao, Weisong Lei, Zhen Zhu, Xiangyu Zhang, Xiao-Yu Biesseck, Bernardo Vidal, Pedro Coelho, Luiz Granada, Roger Menotti, David Computer Vision and Pattern Recognition Artificial Intelligence Computers and Society Machine Learning Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class variability, time and errors produced in manual labeling, and in some cases privacy concerns, among others. This paper presents an overview of the 2nd edition of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at CVPR 2024. FRCSyn aims to investigate the use of synthetic data in face recognition to address current technological limitations, including data privacy concerns, demographic biases, generalization to novel scenarios, and performance constraints in challenging situations such as aging, pose variations, and occlusions. Unlike the 1st edition, in which synthetic data from DCFace and GANDiffFace methods was only allowed to train face recognition systems, in this 2nd edition we propose new sub-tasks that allow participants to explore novel face generative methods. The outcomes of the 2nd FRCSyn Challenge, along with the proposed experimental protocol and benchmarking contribute significantly to the application of synthetic data to face recognition. |
| title | Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2404.10378 |