Latent Distribution Adjusting for Face Anti-Spoofing
Qinghong Sun Zhenfei Yin Yichao Wu Yuanhan Zhang Jing Shao
With the development of deep learning, the field of face anti-spoofing (FAS)
has witnessed great progress. FAS is usually considered a classification
problem, where each class is assumed to contain a single cluster optimized by
softmax loss. In practical deployment, one class can contain several local
clusters, and a single-center is insufficient to capture the inherent structure
of the FAS data. However, few approaches consider large distribution
discrepancies in the field of FAS. In this work, we propose a unified framework
called Latent Distribution Adjusting (LDA) with properties of latent,
discriminative, adaptive, generic to improve the robustness of the FAS model by
adjusting complex data distribution with multiple prototypes. 1) Latent. LDA
attempts to model the data of each class as a Gaussian mixture distribution,
and acquire a flexible number of centers for each class in the last fully
connected layer implicitly. 2) Discriminative. To enhance the intra-class
compactness and inter-class discrepancy, we propose a margin-based loss for
providing distribution constrains for prototype learning. 3) Adaptive. To make
LDA more efficient and decrease redundant parameters, we propose Adaptive
Prototype Selection (APS) by selecting the appropriate number of centers
adaptively according to different distributions. 4) Generic. Furthermore, LDA
can adapt to unseen distribution by utilizing very few training data without
re-training. Extensive experiments demonstrate that our framework can 1) make
the final representation space both intra-class compact and inter-class
separable, 2) outperform the state-of-the-art methods on multiple standard FAS
benchmarks.