Robust face anti-spoofing framework with Convolutional Vision
  Transformer
Yunseung Lee Youngjun Kwak Jinho Shin
Owing to the advances in image processing technology and large-scale
datasets, companies have implemented facial authentication processes, thereby
stimulating increased focus on face anti-spoofing (FAS) against realistic
presentation attacks. Recently, various attempts have been made to improve face
recognition performance using both global and local learning on face images;
however, to the best of our knowledge, this is the first study to investigate
whether the robustness of FAS against domain shifts is improved by considering
global information and local cues in face images captured using self-attention
and convolutional layers. This study proposes a convolutional vision
transformer-based framework that achieves robust performance for various unseen
domain data. Our model resulted in 7.3%$p$ and 12.9%$p$ increases in FAS
performance compared to models using only a convolutional neural network or
vision transformer, respectively. It also shows the highest average rank in
sub-protocols of cross-dataset setting over the other nine benchmark models for
domain generalization.