The virtual-to-real paradigm, ie, training models on virtual data and then applying them to
solve real-world problems, has attracted more and more attention from various domains by …

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 …

State-of-the-art face recognition models show impressive accuracy, achieving over 99.8%
on Labeled Faces in the Wild (LFW) dataset. Such models are trained on large-scale …

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 …

Recent deep face recognition models proposed in the literature utilized large-scale public
datasets such as MS-Celeb-1M and VGGFace2 for training very deep neural networks …

Development of computer vision algorithms using convolutional neural networks and deep
learning has necessitated ever greater amounts of annotated and labelled data to produce …

The great advancements of generative adversarial networks and face recognition models in
computer vision have made it possible to swap identities on images from single sources …

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

Remarkable progress has been achieved in synthesizing photo-realistic images with
generative adversarial networks (GANs). Recently, GANs are utilized as the training sample …