Face Anti-spoofing Based on Supervised Multi-view Contrastive Learning and Two-stage Bilinear Feature Fusion
In this study,a multi-branch network that integrates multi-scale frequency features and depth map features trained by generative adversarial network(GAN)is proposed.Specifically,edge texture information in high-frequency features is beneficial to capturing moire patterns.Low-frequency features are more sensitive to color distortion.Depth maps are more discriminative than RGB images from the visual level as auxiliary information.Supervised multi-view contrastive learning is employed to further enhance multi-view feature learning.Moreover,a two-stage bilinear feature fusion method is proposed to effectively integrate multi-branch features from different views.To evaluate the model,ablation experiments,feature fusion comparison experiments,intra-set experiments and inter-set experiments are conducted on four widely used public datasets,namely CASIA-FASD,Replay-Attack,MSU-MFSD,and OULU-NPU.The experiment result shows that the average HTER of the proposed model on the four tested protocols is 5%(20.3%to 15.0%)better than the DFA method in the inter-set evaluation.
face anti-spoofing(FAS)contrastive learningfeature fusiongenerative adversarial network(GAN)deep learning
NETL
NSTL
万方数据
