A hierarchical multi-modal cross-attention model for face anti-spoofing

Facial recognition has become popular in interactive systems as a means to authenticate identity. However, Facial recognition can be easily attacked illegally through face spoofing. In this paper, we propose a hierarchical multi-modal cross-attention model for face anti-spoofing, which can be flexibly applied in both single-modal and multi-modal scenarios. In order to map features among modalities thoroughly, we also design a novel attention mechanism, namely W-MSA-CA (Window-based Multihead Self-Attention and Cross Attention), which leverages both Multi-modal Multihead Self-Attention (MMSA) and Multi-modal Patch Cross attention (MPCA) to fuse multi-modal features. We test the proposed model on the public datasets and the results show that our model's capability to detect various types of spoofing is effective.