To understand the real world using various types of data, Artificial Intelligence (AI) is the
most used technique nowadays. While finding the pattern within the analyzed data …

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

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

Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical
process of image formation. By inverting such renderer, one can think of a learning …

The large pose discrepancy between two face images is one of the key challenges in face
recognition. Conventional approaches for pose-invariant face recognition either perform …

Despite the large volume of face recognition datasets, there is a significant portion of
subjects, of which the samples are insufficient and thus under-represented. Ignoring such …

We present a new loss function, namely Wing loss, for robust facial landmark localisation
with Convolutional Neural Networks (CNNs). We first compare and analyse different loss …

Face alignment, which fits a face model to an image and extracts the semantic meanings of
facial pixels, has been an important topic in the computer vision community. However, most …

In recent years, the performance of face verification systems has significantly improved using
deep convolutional neural networks (DCNNs). A typical pipeline for face verification includes …

Heatmap regression with a deep network has become one of the mainstream approaches to
localize facial landmarks. However, the loss function for heatmap regression is rarely …