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 …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

Systems incorporating biometric technologies have become ubiquitous in personal,
commercial, and governmental identity management applications. Both cooperative (eg …

In this article, we provide a detailed survey of 3D Morphable Face Models over the 20 years
since they were first proposed. The challenges in building and applying these models …

Algorithmic fairness is frequently motivated in terms of a trade-off in which overall
performance is decreased so as to improve performance on disadvantaged groups where …

It is well known that deep learning approaches to face recognition suffer from various biases
in the available training data. In this work, we demonstrate the large potential of synthetic …

Measuring algorithmic bias is crucial both to assess algorithmic fairness and to guide the
improvement of algorithms. Current bias measurement methods in computer vision are …

Although synthetic training data has been shown to be beneficial for tasks such as human
pose estimation, its use for RGB human action recognition is relatively unexplored. Our goal …

The use of cameras and computational algorithms for noninvasive, low-cost and scalable
measurement of physiological (eg, cardiac and pulmonary) vital signs is very attractive …

Deep-learning (DL) performs well in computer-vision and medical-imaging automated
decision-making applications. A bottleneck of DL stems from the large amount of labelled …