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FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data

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arxiv 2311.10476 v1 pith:DJCCZV6P submitted 2023-11-17 cs.CV

Pietro Melzi , Ruben Tolosana , Ruben Vera-Rodriguez , Minchul Kim , Christian Rathgeb , Xiaoming Liu , Ivan DeAndres-Tame , Aythami Morales
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Julian Fierrez Javier Ortega-Garcia Weisong Zhao Xiangyu Zhu Zheyu Yan Xiao-Yu Zhang Jinlin Wu Zhen Lei Suvidha Tripathi Mahak Kothari Md Haider Zama Debayan Deb Bernardo Biesseck Pedro Vidal Roger Granada Guilherme Fickel Gustavo Führ David Menotti Alexander Unnervik Anjith George Christophe Ecabert Hatef Otroshi Shahreza Parsa Rahimi Sébastien Marcel Ioannis Sarridis Christos Koutlis Georgia Baltsou Symeon Papadopoulos Christos Diou Nicolò Di Domenico Guido Borghi Lorenzo Pellegrini Enrique Mas-Candela Ángela Sánchez-Pérez Andrea Atzori Fadi Boutros Naser Damer Gianni Fenu Mirko Marras
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classification cs.CV
keywords challengedatafacerecognitionfrcsynsynthetictechnologylimitations
verification ladder T0 review T1 audit T2 compute T3 formal
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Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail. This paper offers an overview of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at WACV 2024. This is the first international challenge aiming to explore the use of synthetic data in face recognition to address existing limitations in the technology. Specifically, the FRCSyn Challenge 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. The results achieved in the FRCSyn Challenge, together with the proposed benchmark, contribute significantly to the application of synthetic data to improve face recognition technology.

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  1. Accuracy and Fairness of Facial Recognition Technology in Low-Quality Police Images: An Experiment With Synthetic Faces

    cs.CV 2025-05 conditional novelty 6.0 of 10

    Under controlled synthetic degradations, facial recognition false negatives rise steeply with blur and low resolution, and error rates are highest for Black women, while false positives peak at near-baseline image quality.

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