%0 Journal Article %A GONG Xiao-Feng %A KONG Tiao %A LI Wei-An %A OU Wei-Hua %T Face anti-spoofing based on multi-modal and multi-scale features fusion %D 2022 %R 10.19682/j.cnki.1005-8885.2022.1004 %J The Journal of China Universities of Posts and Telecommunications %P 73-82 %V 29 %N 6 %X Face anti-spoofing is used to assist face recognition system to judge whether the detected face is real face or fake face. In the traditional face anti-spoofing methods, features extracted by hand are used to describe the difference between living face and fraudulent face. But these handmade features do not apply to different variations in an unconstrained environment. The convolutional neural network (CNN) for face deceptions achieves considerable results. However, most existing neural network-based methods simply use neural networks to extract single-scale features from single-modal data, while ignoring multi-scale and multi-modal information. To address this problem, a novel face anti-spoofing method based on multi-modal and multi-scale features fusion ( MMFF) is proposed. Specifically, first residual network ( Resnet )-34 is adopted to extract features of different scales from each modality, then these features of different scales are fused by feature pyramid network (FPN), finally squeeze-and-excitation fusion ( SEF) module and self-attention network ( SAN) are combined to fuse features from different modalities for classification. Experiments on the CASIA-SURF dataset show that the new method based on MMFF achieves better performance compared with most existing methods. %U https://jcupt.bupt.edu.cn/EN/10.19682/j.cnki.1005-8885.2022.1004