Dual-Branch Meta-Learning Network With Distribution Alignment for Face Anti-Spoofing

Existing face anti-spoofing (FAS) methods fail to generalize well to unseen domains with different data distribution from the training domains, due to the distribution discrepancies between various domains. To extract domain-invariant features for unseen domains, this work proposes a Dual-Branch Meta-learning Network (DBMNet) with distribution alignment for face anti-spoofing. Specifically, DBMNet consists of a feature embedding (FE) branch and a depth estimating (DE) branch for real and fake face discrimination. Each branch acts as a meta-learner and is optimized by step-adjusted meta-learning that can adaptively select the best number of meta-train steps. In order to mitigate distribution discrepancies between domains, we introduce two distribution alignment losses to directly regularize the two meta-learners, i.e. , the triplet loss for FE branch and the depth loss for DE branch, respectively. Both of them are designed as part of the meta-train and meta-test objectives, which contribute to higher-order derivatives on the parameters during the meta-optimization for further seeking domain-invariant features. Extensive ablation studies and comparisons with the state-of-the-art methods show the effectiveness of our method for better generalization.