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research-article

FRCSyn-onGoing: : Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems

Volume 107, Issue C

https://doi.org/10.1016/j.inffus.2024.102322

Published: 02 July 2024 Publication History

Abstract

This article presents FRCSyn-onGoing, an ongoing challenge for face recognition where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases and standard experimental protocols. FRCSyn-onGoing is based on the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first face recognition international challenge aiming to explore the use of real and synthetic data independently, and also their fusion, in order to address existing limitations in the technology. Specifically, FRCSyn-onGoing targets concerns related to data privacy issues, demographic biases, generalization to unseen scenarios, and performance limitations in challenging scenarios, including significant age disparities between enrollment and testing, pose variations, and occlusions. To enhance face recognition performance, FRCSyn-onGoing strongly advocates for information fusion at various levels, starting from the input data, where a mix of real and synthetic domains is proposed for specific tasks of the challenge. Additionally, participating teams are allowed to fuse diverse networks within their proposed systems to improve the performance. In this article, we provide a comprehensive evaluation of the face recognition systems and results achieved so far in FRCSyn-onGoing. The results obtained in FRCSyn-onGoing, together with the proposed public ongoing benchmark, contribute significantly to the application of synthetic data to improve face recognition technology.

Highlights

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Challenges in face recognition: Privacy, demographic bias, and generalization.

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Synthetic data offer privacy and large-scale databases with desired characteristics.

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FRCSyn-onGoing: first challenge investigates face recognition and synthetic data.

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Fusion of real and synthetic data, among others, can mitigate several challenges.

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Cited By

Sarridis IKoutlis CPapadopoulos SDiou CGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)FaceX: Understanding Face Attribute Classifiers through Summary Model ExplanationsProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658007(758-766)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658007

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cover image Information Fusion

Information Fusion Volume 107, Issue C

Jul 2024

522 pages

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Elsevier Science Publishers B. V.

Netherlands

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Published: 02 July 2024

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Sarridis IKoutlis CPapadopoulos SDiou CGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)FaceX: Understanding Face Attribute Classifiers through Summary Model ExplanationsProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658007(758-766)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658007

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Affiliations

Pietro Melzi

Universidad Autonoma de Madrid, Spain

Ruben Tolosana

Universidad Autonoma de Madrid, Spain

Ruben Vera-Rodriguez

Universidad Autonoma de Madrid, Spain

Minchul Kim

Michigan State University, US

Christian Rathgeb

Hochschule Darmstadt, Germany

Xiaoming Liu

Michigan State University, US

Ivan DeAndres-Tame

Universidad Autonoma de Madrid, Spain

Aythami Morales

Universidad Autonoma de Madrid, Spain

Julian Fierrez

Universidad Autonoma de Madrid, Spain

Javier Ortega-Garcia

Universidad Autonoma de Madrid, Spain

Weisong Zhao

IIE, CAS, China

School of Cyber Security, UCAS, China

Xiangyu Zhu

MAIS, CASIA, China

School of Artificial Intelligence, UCAS, China

Zheyu Yan

MAIS, CASIA, China

Xiao-Yu Zhang

IIE, CAS, China

School of Cyber Security, UCAS, China

Jinlin Wu

CAIR, HKISI, CAS, China

Zhen Lei

MAIS, CASIA, China

School of Artificial Intelligence, UCAS, China

CAIR, HKISI, CAS, China

Suvidha Tripathi

LENS, Inc., US

Mahak Kothari

LENS, Inc., US

Md Haider Zama

LENS, Inc., US

Debayan Deb

LENS, Inc., US

Bernardo Biesseck

Federal University of Paraná, Curitiba, PR, Brazil

Federal Institute of Mato Grosso, Pontes e Lacerda, Brazil

Pedro Vidal

Federal University of Paraná, Curitiba, PR, Brazil

Roger Granada

unico - idTech, Brazil

Guilherme Fickel

unico - idTech, Brazil

Gustavo Führ

unico - idTech, Brazil

David Menotti

Federal University of Paraná, Curitiba, PR, Brazil

Alexander Unnervik

Idiap Research Institute, Switzerland

École Polytechnique Fédérale de Lausanne, Switzerland

Anjith George

Idiap Research Institute, Switzerland

Christophe Ecabert

Idiap Research Institute, Switzerland

Hatef Otroshi Shahreza

Idiap Research Institute, Switzerland

École Polytechnique Fédérale de Lausanne, Switzerland

Parsa Rahimi

Idiap Research Institute, Switzerland

École Polytechnique Fédérale de Lausanne, Switzerland

Sébastien Marcel

Idiap Research Institute, Switzerland

Université de Lausanne, Switzerland

Ioannis Sarridis

Centre for Research and Technology Hellas, Greece

Christos Koutlis

Centre for Research and Technology Hellas, Greece

Georgia Baltsou

Centre for Research and Technology Hellas, Greece

Symeon Papadopoulos

Centre for Research and Technology Hellas, Greece

Christos Diou

Harokopio University of Athens, Greece

Nicolò Di Domenico

University of Bologna, Cesena Campus, Italy

Guido Borghi

University of Bologna, Cesena Campus, Italy

Lorenzo Pellegrini

University of Bologna, Cesena Campus, Italy

Enrique Mas-Candela

Facephi, Spain

Ángela Sánchez-Pérez

Facephi, Spain

Andrea Atzori

University of Cagliari, Italy

Fadi Boutros

Fraunhofer IGD, Germany

TU Darmstadt, Germany

Naser Damer

Fraunhofer IGD, Germany

TU Darmstadt, Germany

Gianni Fenu

University of Cagliari, Italy

Mirko Marras

University of Cagliari, Italy

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