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CA-MoEiT: Generalizable Face Anti-spoofing via Dual Cross-Attention and Semi-fixed Mixture-of-Expert

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Abstract

Although the generalization of face anti-spo-ofing (FAS) is increasingly concerned, it is still in the initial stage to solve it based on Vision Transformer (ViT). In this paper, we present a cross-domain FAS framework, dubbed the Transformer with dual Cross-Attention and semi-fixed Mixture-of-Expert (CA-MoEiT), for stimulating the generalization of Face Anti-Spoofing (FAS) from three aspects: (1) Feature augmentation. We insert a MixStyle after PatchEmbed layer to synthesize diverse patch embeddings from novel domains and enhance the generalizability of the trained model. (2) Feature alignment. We design a dual cross-attention mechanism which extends the self-attention to align the common representation from multiple domains. (3) Feature complement. We design a semi-fixed MoE (SFMoE) to selectively replace MLP by introducing a fixed super expert. Benefiting from the gate mechanism in SFMoE, professional experts are adaptively activated with independent learning domain-specific information, which is used as a supplement to domain-invariant features learned by the super expert to further improve the generalization. It is important that the above three technologies can be compatible with any variant of ViT as plug-and-play modules. Extensive experiments show that the proposed CA-MoEiT is effective and outperforms the state-of-the-art methods on several public datasets.

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  1. The State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China

    Ajian Liu

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Liu, A. CA-MoEiT: Generalizable Face Anti-spoofing via Dual Cross-Attention and Semi-fixed Mixture-of-Expert. Int J Comput Vis 132, 5439–5452 (2024). https://doi.org/10.1007/s11263-024-02135-2

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