Recently, generative adversarial networks (GANs) have progressed enormously, which
makes them able to learn complex data distributions in particular faces. More and more …

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

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

Image inpainting has been widely exploited in the field of computer vision and image
processing. The main purpose of image inpainting is to produce visually plausible structure …

As an emerging topic in face recognition, designing margin-based loss functions can
increase the feature margin between different classes for enhanced discriminability. More …

We propose an approach for face image generation of virtual people with disentangled,
precisely-controllable latent representations for identity of non-existing people, expression …

Deep learning, in particular the deep convolutional neural networks, has received
increasing interests in face recognition recently, and a number of deep learning methods …

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 …

We propose a straightforward method that simultaneously reconstructs the 3D facial
structure and provides dense alignment. To achieve this, we design a 2D representation …

Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …