Abstract Face Anti-Spoofing (FAS) is crucial for securing face recognition systems against
presentation attacks. With advancements in sensor manufacture and multi-modal learning …

Multi-Source-Free Unsupervised Domain Adaptation (MSFUDA) requires aggregating
knowledge from multiple source models and adapting it to the target domain. Two …

With the growing popularity of facial recognition (FR) in access control systems, there has
been a corresponding increase in presentation attacks (PAs) to gain unauthorized access …

Palmprint as biometrics has gained increasing attention recently due to its discriminative
ability and robustness. However, existing methods mainly improve palmprint verification …

Real-world face recognition systems are vulnerable to diverse face attacks, ranging from
digitally manipulated artifacts to physically crafted spoofing attacks. Existing works primarily …

In recent years, domain adaptation techniques have been widely used to adapt face anti-
spoofing models to a cross-scenario target domain. Most previous methods assume that the …

Face anti-spoofing is a critical component of face recognition technology. However, it suffers
from poor generalizability for cross-scenario target domains due to the simultaneous …

Although face recognition technology has been applied in many scenarios, it still suffers
from many types of presentation attacks, so face anti-spoofing (FAS) becomes a hot topic in …

Graph domain adaptation (GDA) aims to address the challenge of limited label data in the
target graph domain. Existing methods such as UDAGCN, GRADE, DEAL, and COCO for …

With the growing availability of databases for face presentation attack detection, researchers
are increasingly focusing on video-based face anti-spoofing methods that involve hundreds …