FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)(2023)
Abstract
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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Key words
Face Recognition,Era Of Data,Challenges In The Era,Benchmark,Application Of Data,Facial Recognition Technology,Training Data,Ethnic Groups,Learning Rate,Validation Set,Variables Age,Average Accuracy,Data Augmentation,Stochastic Gradient Descent,System Description,Generative Adversarial Networks,Diffusion Model,Demographic Groups,Domain Adaptation,Random Flipping,Real Database,Presence Of Occlusion,Accuracy Verification,Baseline System,Synthetic Training Data,Deep Embedding,Challenging Conditions,Intra-class Variance,Standard Deviation Of Accuracy,Loss Function
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