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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.01383 |
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| _version_ | 1866918240177881088 |
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| author | DeAndres-Tame, Ivan Tolosana, Ruben Melzi, Pietro Vera-Rodriguez, Ruben Kim, Minchul Rathgeb, Christian Liu, Xiaoming Gomez, Luis F. 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 Gomez, Luis F. 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 popularity for face recognition technologies, mainly due to the privacy concerns and challenges associated with obtaining real data, including diverse scenarios, quality, and demographic groups, among others. It also offers some advantages over real data, such as the large amount of data that can be generated or the ability to customize it to adapt to specific problem-solving needs. To effectively use such data, face recognition models should also be specifically designed to exploit synthetic data to its fullest potential. In order to promote the proposal of novel Generative AI methods and synthetic data, and investigate the application of synthetic data to better train face recognition systems, we introduce the 2nd FRCSyn-onGoing challenge, based on the 2nd Face Recognition Challenge in the Era of Synthetic Data (FRCSyn), originally launched at CVPR 2024. This is an ongoing challenge that provides researchers with an accessible platform to benchmark i) the proposal of novel Generative AI methods and synthetic data, and ii) novel face recognition systems that are specifically proposed to take advantage of synthetic data. We focus on exploring the use of synthetic data both individually and in combination with real data to solve current challenges in face recognition such as demographic bias, domain adaptation, and performance constraints in demanding situations, such as age disparities between training and testing, changes in the pose, or occlusions. Very interesting findings are obtained in this second edition, including a direct comparison with the first one, in which synthetic databases were restricted to DCFace and GANDiffFace. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01383 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data DeAndres-Tame, Ivan Tolosana, Ruben Melzi, Pietro Vera-Rodriguez, Ruben Kim, Minchul Rathgeb, Christian Liu, Xiaoming Gomez, Luis F. 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 popularity for face recognition technologies, mainly due to the privacy concerns and challenges associated with obtaining real data, including diverse scenarios, quality, and demographic groups, among others. It also offers some advantages over real data, such as the large amount of data that can be generated or the ability to customize it to adapt to specific problem-solving needs. To effectively use such data, face recognition models should also be specifically designed to exploit synthetic data to its fullest potential. In order to promote the proposal of novel Generative AI methods and synthetic data, and investigate the application of synthetic data to better train face recognition systems, we introduce the 2nd FRCSyn-onGoing challenge, based on the 2nd Face Recognition Challenge in the Era of Synthetic Data (FRCSyn), originally launched at CVPR 2024. This is an ongoing challenge that provides researchers with an accessible platform to benchmark i) the proposal of novel Generative AI methods and synthetic data, and ii) novel face recognition systems that are specifically proposed to take advantage of synthetic data. We focus on exploring the use of synthetic data both individually and in combination with real data to solve current challenges in face recognition such as demographic bias, domain adaptation, and performance constraints in demanding situations, such as age disparities between training and testing, changes in the pose, or occlusions. Very interesting findings are obtained in this second edition, including a direct comparison with the first one, in which synthetic databases were restricted to DCFace and GANDiffFace. |
| title | Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2412.01383 |