  Enhancing the domain generalization performance of Face Anti-Spoofing (FAS)
techniques has emerged as a research focus. Existing methods are dedicated to
extracting domain-invariant features from various training domains. Despite the
promising performance, the extracted features inevitably contain residual style
feature bias (e.g., illumination, capture device), resulting in inferior
generalization performance. In this paper, we propose an alternative and
effective solution, the Textually Guided Domain Generalization (TeG-DG)
framework, which can effectively leverage text information for cross-domain
alignment. Our core insight is that text, as a more abstract and universal form
of expression, can capture the commonalities and essential characteristics
across various attacks, bridging the gap between different image domains.
Contrary to existing vision-language models, the proposed framework is
elaborately designed to enhance the domain generalization ability of the FAS
task. Concretely, we first design a Hierarchical Attention Fusion (HAF) module
to enable adaptive aggregation of visual features at different levels; Then, a
Textual-Enhanced Visual Discriminator (TEVD) is proposed for not only better
alignment between the two modalities but also to regularize the classifier with
unbiased text features. TeG-DG significantly outperforms previous approaches,
especially in situations with extremely limited source domain data (~14% and
~12% improvements on HTER and AUC respectively), showcasing impressive few-shot
performance.
