计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 409-415.doi: 10.11896/jsjkx.210100181

• 图像处理& 多媒体技术 • 上一篇    下一篇

基于多特征融合的人脸活体检测算法

栾晓, 李晓双   

  1. 重庆邮电大学计算机科学与技术学院 重庆400065
    重庆邮电大学图像认知重庆市重点实验室 重庆400065
  • 出版日期:2021-11-10 发布日期:2021-11-12
  • 通讯作者: 栾晓(luanxiao@cqupt.edu.cn)
  • 基金资助:
    国家自然科学基金(61801068)

Face Anti-spoofing Algorithm Based on Multi-feature Fusion

LUAN Xiao, LI Xiao-shuang   

  1. College of Computer Science,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
    Chongqing Key Laboratory of Image Cognition,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:LUAN Xiao,born in 1983,Ph.D,asso-ciate professor,is a member of China Computer Federation.His main research interests include face recognition,medical image processing and machine learning.
  • Supported by:
    National Natural Science Foundation of China(61801068).

摘要: 近年来,随着人脸识别系统的不断发展,各种假冒合法用户的欺骗手段不断出现。基于单一差异线索进行的活体检测,已经不能满足当前复杂环境下提高人脸活体检测方法性能的需求。基于此,文中提出多特征融合的方法,使用卷积神经网络从人脸图像的不同线索中学习多个特征来进行活体检测,深度图在空间上能够区分真假人脸之间的深度信息;光流图在时间上能够区分真假人脸之间的动态信息;残差噪声图根据真人脸的一次成像和假冒人脸的二次成像噪声成分的不同进行区分。文中融合3种特征,不仅利用空间、时间多维度线索弥补了单一线索的不足,同时也提高了模型的泛化能力。相比现有的方法,所提方法无论是在同一个数据库还是跨数据库的情况下,均有较好的实验结果。具体而言,所提方法在CASIA数据集、REPLAY-ATTACK数据集和NUAA数据集上的错误率分别为0.11%,0.06%和0.45%。

关键词: 多特征融合, 活体检测, 人脸识别

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

中图分类号: 

  • TP391.41
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