The performance of face recognition system degrades when the variability of the acquired
faces increases. Prior work alleviates this issue by either monitoring the face quality in pre …

State-of-the-art face recognition models show impressive accuracy, achieving over 99.8%
on Labeled Faces in the Wild (LFW) dataset. Such models are trained on large-scale …

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

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 …

Face recognition has witnessed significant progress due to the advances of deep
convolutional neural networks (CNNs), the central task of which is how to improve the …

Face recognition systems are usually faced with unseen domains in real-world applications
and show unsatisfactory performance due to their poor generalization. For example, a well …

Face anti-spoofing is essential to prevent face recognition systems from a security breach.
Much of the progresses have been made by the availability of face anti-spoofing benchmark …

Face anti-spoofing is essential to prevent face recognition systems from a security breach.
Much of the progresses have been made by the availability of face anti-spoofing benchmark …

Abstract Vision Transformers (ViTs) have demonstrated powerful representation ability in
various visual tasks thanks to their intrinsic data-hungry nature. However, we unexpectedly …

Face recognition has achieved significant progress with the growing scale of collected
datasets, which empowers us to train strong convolutional neural networks (CNNs). While a …