Deep neural networks have become prevalent in human analysis, boosting the performance
of applications, such as biometric recognition, action recognition, as well as person re …

The advent of smart cities has paved the way for transformative advancements in healthcare,
particularly in the domain of disease diagnosis. In the wake of the COVID-19 pandemic …

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

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 …

The advent of artificial intelligence-generated content (AIGC) represents a pivotal moment in
the evolution of information technology. With AIGC, it can be effortless to generate high …

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 (DL)-based methods have recently shown great promise in bitemporal
change detection (CD). Existing discriminative methods based on convolutional neural …

Synthetic data is emerging as a substitute for authentic data to solve ethical and legal
challenges in handling authentic face data. The current models can create real-looking face …

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 …