Hyperbolic Face Anti-Spoofing
Shuangpeng Han Rizhao Cai Yawen Cui Zitong Yu Yongjian Hu Alex Kot
Learning generalized face anti-spoofing (FAS) models against presentation
attacks is essential for the security of face recognition systems. Previous FAS
methods usually encourage models to extract discriminative features, of which
the distances within the same class (bonafide or attack) are pushed close while
those between bonafide and attack are pulled away. However, these methods are
designed based on Euclidean distance, which lacks generalization ability for
unseen attack detection due to poor hierarchy embedding ability. According to
the evidence that different spoofing attacks are intrinsically hierarchical, we
propose to learn richer hierarchical and discriminative spoofing cues in
hyperbolic space. Specifically, for unimodal FAS learning, the feature
embeddings are projected into the Poincar\'e ball, and then the hyperbolic
binary logistic regression layer is cascaded for classification. To further
improve generalization, we conduct hyperbolic contrastive learning for the
bonafide only while relaxing the constraints on diverse spoofing attacks. To
alleviate the vanishing gradient problem in hyperbolic space, a new feature
clipping method is proposed to enhance the training stability of hyperbolic
models. Besides, we further design a multimodal FAS framework with Euclidean
multimodal feature decomposition and hyperbolic multimodal feature fusion &
classification. Extensive experiments on three benchmark datasets (i.e., WMCA,
PADISI-Face, and SiW-M) with diverse attack types demonstrate that the proposed
method can bring significant improvement compared to the Euclidean baselines on
unseen attack detection. In addition, the proposed framework is also
generalized well on four benchmark datasets (i.e., MSU-MFSD, IDIAP
REPLAY-ATTACK, CASIA-FASD, and OULU-NPU) with a limited number of attack types.