Unsupervised Feature Disentanglement and Augmentation Network for One-class Face Anti-spoofing
Journal:
arXiv
Published Date:
Mar 29, 2025
Abstract
Face anti-spoofing (FAS) techniques aim to enhance the security of facial
identity authentication by distinguishing authentic live faces from deceptive
attempts. While two-class FAS methods risk overfitting to training attacks to
achieve better performance, one-class FAS approaches handle unseen attacks well
but are less robust to domain information entangled within the liveness
features. To address this, we propose an Unsupervised Feature Disentanglement
and Augmentation Network (\textbf{UFDANet}), a one-class FAS technique that
enhances generalizability by augmenting face images via disentangled features.
The \textbf{UFDANet} employs a novel unsupervised feature disentangling method
to separate the liveness and domain features, facilitating discriminative
feature learning. It integrates an out-of-distribution liveness feature
augmentation scheme to synthesize new liveness features of unseen spoof
classes, which deviate from the live class, thus enhancing the representability
and discriminability of liveness features. Additionally, \textbf{UFDANet}
incorporates a domain feature augmentation routine to synthesize unseen domain
features, thereby achieving better generalizability. Extensive experiments
demonstrate that the proposed \textbf{UFDANet} outperforms previous one-class
FAS methods and achieves comparable performance to state-of-the-art two-class
FAS methods.
