计算机应用与软件2024，Vol.41Issue(12)：188-192,5.DOI:10.3969/j.issn.1000-386x.2024.12.027
基于时序光流与微表情的人脸活体识别
FACE RECOGNITION IN VIVO BASED ON TEMPORAL OPTICAL FLOW AND MICRO-EXPRESSION
摘要
Abstract
Insufficient generalization and complexity in face anti-spoofing detection models results in a poor performance targeting on new face attack algorithm.Therefore,a face recognition model in vivo(FT-CNN)is proposed based on optical flow estimate and micro-expression in face.The model consisted of TVNet-DTSCNN and Attention CNN-LSTM.TVNet-DTSCNN performed optical flow prediction and micro-expression extraction on the input time-series face frames,and attention CNN-LSTM extracted and magnified the motion detail cues in the face video,which made the model to learn the robust feature for both live and prosthetic faces.Experiments on CASIA,CASIA-SURF and MSU-MFSD datasets indicate that the performance of FT-CNN in accuracy(Acc),average error rate(HTER)and generalization is significantly improved compared with the previous models.关键词
人脸活体检测/微表情识别/注意力机制/3D卷积网络/光流预测Key words
Face anti-spoofing detection/Mirco-expression recognition/Attention mechanism/3D CNN/Optical flow estimate分类
信息技术与安全科学引用本文复制引用
周延森,徐传凯,崔见泉..基于时序光流与微表情的人脸活体识别[J].计算机应用与软件,2024,41(12):188-192,5.基金项目
国际关系学院国家安全高精尖学科建设科研专项(2019GA38). （2019GA38）
