Attention-Aware Dual-Stream Network for Multimodal Face Anti-Spoofing.

Since the rapid development of face recognition systems using 3D cameras, the public has demanded great safety regulations for these devices. As a closely related topic, multimodal face anti-spoofing (FAS) has become an indispensable part of face recognition systems. However, existing multimodal FAS tools suffer from performance degradation under external low-lighting conditions and insufficient representation capabilities of fusion features. To address these issues, we present an attention-aware dual-stream fusion method using 3D cameras (i.e., IR+Depth) and considering both fine-grained and global features. Specifically, we introduce a surface normal generator using depth maps to obtain robust and discriminative representations. Then, we leverage the attention mechanism to split each stream into two branches. The first branch explores complementary global information between different modalities, while the second branch captures subtle and local features from each modality. The system regards multimodal FAS as a fine-grained classification problem. Moreover, to ensure that local areas in the image do not overlap and belong to the same class, a joint loss function is developed and proven to further boost the performance of FAS. We extensively evaluate our proposed strategies on various multimodal databases, and the results show that when compared with current state-of-the-art multimodal methods, our framework achieves superior performance.