  This paper presents a novel perspective for enhancing anti-spoofing
performance in zero-shot data domain generalization. Unlike traditional image
classification tasks, face anti-spoofing datasets display unique generalization
characteristics, necessitating novel zero-shot data domain generalization. One
step forward to the previous frame-wise spoofing prediction, we introduce a
nuanced metric calculation that aggregates frame-level probabilities for a
video-wise prediction, to tackle the gap between the reported frame-wise
accuracy and instability in real-world use-case. This approach enables the
quantification of bias and variance in model predictions, offering a more
refined analysis of model generalization. Our investigation reveals that simply
scaling up the backbone of models does not inherently improve the mentioned
instability, leading us to propose an ensembled backbone method from a Bayesian
perspective. The probabilistically ensembled backbone both improves model
robustness measured from the proposed metric and spoofing accuracy, and also
leverages the advantages of measuring uncertainty, allowing for enhanced
sampling during training that contributes to model generalization across new
datasets. We evaluate the proposed method from the benchmark OMIC dataset and
also the public CelebA-Spoof and SiW-Mv2. Our final model outperforms existing
state-of-the-art methods across the datasets, showcasing advancements in Bias,
Variance, HTER, and AUC metrics.
