Face anti-spoofing (FAS) plays a critical role in securing face recognition systems from
different presentation attacks. Previous works leverage auxiliary pixel-level supervision and …

Face anti-spoofing (FAS) or presentation attack detection is an essential component of face
recognition systems deployed in security-critical applications. Existing FAS methods have …

Existing face anti-spoofing (FAS) models have achieved high performance on specific
datasets. However, for the application of real-world systems, the FAS model should …

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 …

Systems capable of analyzing and quantifying human physical or behavioral traits, known as
biometrics systems, are growing in use and application variability. Since its evolution from …

Tools to generate high quality synthetic speech signal that is perceptually indistinguishable
from speech recorded from human speakers are easily available. Several approaches have …

For safe motion planning in real-world autonomous vehicles require behavior prediction
models that are reliable and robust to distribution shifts. The recent studies suggest that the …

Face anti-spoofing (FAS) is to protect facial recognition systems against presentation
attacks. However recent research on FAS often neglects real-world conditions such as …

Face anti-spoofing aims to counter facial presentation attacks and heavily relies on
identifying live/spoof discriminative features. In this paper, we propose a novel Learnable …

Along with the widespread use of face recognition systems, their vulnerability has become
highlighted. While existing face anti-spoofing methods can be generalized between attack …