基于内容风格增强和特征嵌入优化的人脸活体检测方法OA北大核心CSTPCD

In response to the issues of inadequate feature representation in existing face anti-spoofing algorithms and poor cross-dataset generalization performance,this paper proposed a face anti-spoofing method based on content-style enhancement and feature embedding optimization.Firstly,this method utilized a ResNet-18 encoder to extract generic features from multiple source domains,and then subjected to separation through two self-adaptive modules with different attention mechanisms,en-hancing the representation of global content features and local style features.Secondly,based on the AdaIN algorithm,it or-ganically fused content features with style features,further improving the feature representation,and the fused features were subsequently input to specific classifiers and domain discriminators for adversarial training.Finally,by employing average neg-ative samples and semi-hard sample triplet mining to optimize feature embeddings,effectively striking a balance between intra-class cohesion and inter-class discrimination,better capturing the boundaries between genuine and spoofed categories.The proposed method was trained and tested on four benchmark datasets,suchas CASIA-FASD,REPLAY-ATTACK,MSU-MFSD,and OULU-NPU.It achieves accuracy of 6.33％,12.05％,8.38％and 10.59％respectively,which are superior to existing algorithms.This indicates that the proposed method can significantly improve the generalization performance of face live detec-tion models in cross-dataset testing.