Face recognition is one of the most active research fields of computer vision and pattern
recognition, with many practical and commercial applications including identification, access …

Over the last years deep learning methods have been shown to outperform previous state‐of‐
the‐art machine learning techniques in several fields, with computer vision being one of the …

Learning discriminative face features plays a major role in building high-performing face
recognition models. The recent state-of-the-art face recognition solutions proposed to …

This paper introduces a powerful channel augmented joint learning strategy for the visible-
infrared recognition problem. For data augmentation, most existing methods directly adopt …

Artefacts that differentiate spoofed from bona-fide utterances can reside in specific temporal
or spectral intervals. Their reliable detection usually depends upon computationally …

In this paper, we contribute a new million-scale face benchmark containing noisy 4M
identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) …

Despite being robust to small amounts of label noise, convolutional neural networks trained
with stochastic gradient methods have been shown to easily fit random labels. When there …

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

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 …

Modeling data uncertainty is important for noisy images, but seldom explored for face
recognition. The pioneer work, PFE, considers uncertainty by modeling each face image …