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 …

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 …

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 …

Our goal with this survey is to provide an overview of the state of the art deep learning
methods for face generation and editing using StyleGAN. The survey covers the evolution of …

This study investigates the possibility of mitigating the demographic biases that affect face
recognition technologies through the use of synthetic data. Demographic biases have the …

Large-scale face recognition datasets are collected by crawling the Internet and without
individuals' consent, raising legal, ethical, and privacy concerns. With the recent advances in …

Artificial Intelligence (AI) has seamlessly integrated into numerous scientific domains,
catalysing unparalleled enhancements across a broad spectrum of tasks; however, its …

Abstract Diffusion Models (DMs) have emerged as powerful generative models with
unprecedented image generation capability. These models are widely used for data …

Biometric systems based on iris recognition are currently being used in border control
applications and mobile devices. However, research in iris recognition is stymied by various …

We address the need for a large-scale database of children's faces by using generative
adversarial networks (GANs) and face-age progression (FAP) models to synthesize a …