摘要
人脸活体检测技术作为人脸识别系统安全运行的重要保障,对保障网络空间安全意义重大.针对基于视频的人脸欺骗攻击,提出一种基于局部二值模式-多层离散余弦变换(local binary pattern and multilayer discrete cosine transform,LBP-MDCT)和卷积神经网络(convolutional neural network,CNN)融合的人脸活体检测算法.首先从检测视频中提取人脸图像;接着对人脸图像进行LBP和多层DCT变换以得到LBP-MDC T特征,将部分人脸图像输入CNN中以得到CNN特征;然后将两种特征分别输入到支持向量机(support vector machine,SVM)中得到分类结果;最后将SVM的输出进行决策级融合以判定检测视频的合法性.在Replay-Attack和CASIA-FASD数据库上的实验结果表明,相对于现有算法,该算法的检测性能更加优越.
In applications of cyberspace security,face liveness detection is significant for face recognition systems.In this paper,an innovative face anti-spoofing algorithm against video-based face spoofing attacks is proposed based on local binary patterns and multilayer discrete cosine transform(LBP-MDCT)and convolutional neural network(CNN).First,we first extract face images from a target video,generate LBP features for each extracted face image and perform multilayer DCT onto the features to obtain LBP-MDCT features.Second,we input part of face images into CNN to obtain CNN features.After that,the two types of features are respectively input into support vector machine(SVM)classifier.In the last stage,the SVM output is fused with a decision-level operation to determine whether the target video is a spoof attack or a valid access.Compared with existing algorithms,the experimental results on two benchmarking datasets(Replay-Attack dataset and CASIA-FASD dataset)demonstrate the excellent effectiveness of the proposed method.
作者
刘伟
章琬苓
项世军
LIU Wei;ZHANG Wanling;XIANG Shijun(School of Information Science and Technology,Jinan University,Guangzhou 510632,China)
出处
《应用科学学报》
CAS
CSCD
北大核心
2019年第5期609-617,共9页
Journal of Applied Sciences
基金
国家自然科学基金面上项目(No.61772234)
2018年广东大学生科技创新培育专项资金项目(No.pdjhb0061)资助
关键词
人脸活体检测
局部二值模式
多层离散余弦变换
卷积神经网络
决策级融合
face anti-spoofing
local binary pattern (LBP)
multilayer discrete Cosinetransform (DCT)
convolutional neural network (CNN)
decision-level fusion
