基于多特征融合的人脸活体检测算法

doi:10.11896/jsjkx.210100181

Abstract

Abstract: In recent years,with the development of face recognition systems,various spoofing methods that impersonate legitimate users appear.Face anti-spoofing detection method based on a single clue no longer meets the requirements of current face recognition system under complex environment.Based on this,we propose to use a convolutional neural network to learn multi-feature from different clues of face images,and to fuse the depth map,the face optical flow map,and the residual noise map to perform liveness detection.The depth map can distinguish the depth information between real and fake faces in space,the optical flow map can distinguish the dynamic information between real and fake faces in time,the residual noise map is based on the one-time imaging of the real face and the fake face.The secondary imaging noise components are distinguished by different components,and the three features are merged to use space,time and multi-dimensional clues to make up for the shortcomings of a single clue,and also improve the generalization ability of the model.Compared with the existing methods,our method shows promising results both on the single database and cross-databases.Specifically,equal error rate (EER) of our method on databases of CASIA,REPLAY-ATTACK and NUAA can achieve 0.11%,0.06% and 0.45%,respectively.

Key words: Face recognition, Multi-feature fusion, Spoofing detection

CLC Number:

TP391.41

LUAN Xiao, LI Xiao-shuang. Face Anti-spoofing Algorithm Based on Multi-feature Fusion[J].Computer Science, 2021, 48(11A): 409-415.

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