FACE RECOGNITION IN VIVO BASED ON TEMPORAL OPTICAL FLOW AND MICRO-EXPRESSION
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.
Face anti-spoofing detectionMirco-expression recognitionAttention mechanism3D CNNOptical flow estimate
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