Deep learning models currently achieve human levels of performance on real-world face
recognition tasks. We review scientific progress in understanding human face processing …

Biometric recognition technology has made significant advances over the last decade and is
now used across a number of services and applications. However, this widespread …

Over the past two decades, biometric recognition has exploded into a plethora of different
applications around the globe. This proliferation can be attributed to the high levels of …

Explainable face recognition (XFR) is the problem of explaining the matches returned by a
facial matcher, in order to provide insight into why a probe was matched with one identity …

Face recognition networks encode information about sensitive attributes while being trained
for identity classification. Such encoding has two major issues:(a) it makes the face …

3D face reconstruction algorithms from images and videos are applied to many fields, from
plastic surgery to the entertainment sector, thanks to their advantageous features. However …

Although face recognition has made impressive progress in recent years, we ignore the
racial bias of the recognition system when we pursue a high level of accuracy. Previous …

Convolutional Neural Networks (CNNs) are today the de-facto standard for extracting
compact and discriminative face representations (templates) from images in automatic face …

The success of modern face recognition systems is based on the advances of deeply-
learned features. These embeddings aim to encode the identity of an individual such that …

Face Age Progression (FAP) refers to synthesizing face images while simulating ageing
effects, thus enabling predicting the future appearance of an individual. The generation of …