Over the past years, deep learning capabilities and the availability of large-scale training
datasets advanced rapidly, leading to breakthroughs in face recognition accuracy. However …

Generating synthetic datasets for training face recognition models is challenging because
dataset generation entails more than creating high fidelity images. It involves generating …

The availability of large-scale authentic face databases has been crucial to the significant
advances made in face recognition research over the past decade. However, legal and …

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 …

Face recognition systems have significantly advanced in recent years, driven by the
availability of large-scale datasets. However, several issues have recently came up …

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 …

Deep learning has significantly transformed face recognition, enabling the deployment of
large-scale, state-of-the-art solutions worldwide. However, the widespread adoption of deep …

This study addresses the limitations of Transformer models in image feature extraction,
particularly their lack of inductive bias for visual structures. Compared to Convolutional …

Blind face restoration is an important task in computer vision and has gained significant
attention due to its wide-range applications. Previous works mainly exploit facial priors to …

In this paper we address the challenge of making ViT models more robust to unseen affine
transformations. Such robustness becomes useful in various recognition tasks such as face …