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Villegas Nuñez, Paulo AngelAuthorDeandres-Tame I.AuthorTolosana R.AuthorVera-Rodriguez R.AuthorMorales A.AuthorFierrez J.AuthorOrtega-Garcia J.Author

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March 7, 2025
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Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data

Publicated to: Ieee Computer Society Conference On Computer Vision And Pattern Recognition Workshops. 3173-3183 - 2024-01-01 (), DOI: 10.1109/CVPRW63382.2024.00323

Authors:

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, Ebastien; Neto, Pedro C; Huber, Marco; Kolf, Jan Niklas; Damer, Naser; Boutros, Fadi; Cardoso, Jaime S; Sequeira, Ana F; Atzori, Andrea; Fenu, Gianni; Marras, Mirko; Struc, 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
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Affiliations

CASIA, MAIS, Shanghai, Peoples R China - Author
China Telecom AI, Beijing, Peoples R China - Author
Chinese Acad Sci, IIE, Beijing, Peoples R China - Author
Ecole Polytech Fed Lausanne, Lausanne, Switzerland - Author
Fed Inst Mato Grosso, Cuiaba, Brazil - Author
Fraunhofer IGD, Darmstadt, Germany - Author
Fudan Univ, Shanghai, Peoples R China - Author
Hsch Darmstadt, Darmstadt, Germany - Author
ID R&D Inc, New York, NY USA - Author
Idiap Res Inst, Martigny, Switzerland - Author
INESC TEC, Porto, Portugal - Author
Inst for Basic Sci Korea, Daejeon, South Korea - Author
Korea Adv Inst Sci & Technol, Daejeon, South Korea - Author
Michigan State Univ, E Lansing, MI 48824 USA - Author
Netease Inc, Interact Entertainment Grp, Guangzhou, Peoples R China - Author
Samsung Elect China R&D Ctr, Shenzhen, Peoples R China - Author
Tencent Youtu Lab, Shanghai, Peoples R China - Author
Unico IdTech, Sao Paulo, Brazil - Author
Univ Autonoma Madrid, Madrid, Spain - Author
Univ Cagliari, Cagliari, Italy - Author
Univ Fed Parana, Curitiba, Parana, Brazil - Author
Univ Lausanne, Lausanne, Switzerland - Author
Univ Ljubljana, Ljubljana, Slovenia - Author
Univ Porto, Porto, Portugal - Author
Univ Sci & Technol, Hefei, Peoples R China - Author
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Abstract

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 2(nd) 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 1(st) edition, in which synthetic data from DCFace and GANDiffFace methods was only allowed to train face recognition systems, in this 2(nd) edition we propose new subtasks that allow participants to explore novel face generative methods. The outcomes of the 2(nd) FRCSyn Challenge, along with the proposed experimental protocol and benchmarking contribute significantly to the application of synthetic data to face recognition.
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Keywords

BenchmarkingBiometrics recognitionDemographic biasFace recognitionFrcsynGenerative aiPrivacySynthetic data

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Vision and Pattern Recognition.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-05:

  • WoS: 8
  • Scopus: 19
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-05:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 15.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 14 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.
  • The number of mentions on the social network X (formerly Twitter): 2 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Brazil; China; Germany; Italy; Portugal; Republic of Korea; Slovenia; Switzerland; United States of America.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (DE ANDRES TAME, IVAN IOEL) .

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Awards linked to the item

This study has received funding from the European Union's Horizon 2020 TReSPAsS-ETN (No 860813) and is supported by INTER-ACTION (PID2021-126521OB-I00 MICINN/FEDER), Ciatedra ENIA UAMVERIDAS en IA Responsable (NextGenerationEU PRTR TSI-1009272023-2) and R&D Agreement DGGC/UAM/FUAM for Biometrics and Cybersecurity. It is also supported by the German Federal Ministry of Education and Research and the Hessian Ministry of Higher Education, Research, Science and the Arts within their joint support of the National Research Center for Applied Cybersecurity ATHENE. K-IBS-DS was supported by the Institute for Basic Science, Republic of Korea (IBS-R029C2). UNICA-IGD-LSI was supported by the ARIS program P2-0250B.
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