基于微调策略的多线索融合人脸活体检测

doi:10.19678/j.issn.1000-3428.0050721

计算机工程 ›› 2019, Vol. 45 ›› Issue (5): 256-260. doi: 10.19678/j.issn.1000-3428.0050721

基于微调策略的多线索融合人脸活体检测

胡斐^1a,文畅^1a,谢凯^1b,贺建飚²

1.长江大学 a.计算机科学学院; b.电子信息学院,湖北荆州 434023; 2.中南大学信息科学与工程学院,长沙 410083

收稿日期:2018-03-12 出版日期:2019-05-15 发布日期:2019-05-15
作者简介:胡斐(1996—),男,硕士研究生,主研方向为信号处理、图像与视频处理;文畅(通信作者),讲师;谢凯,教授;贺建飚,副教授。
基金资助:
国家自然科学基金(61272147)。

Multi-cue fusion face liveness detection based on fine-tuning strategy

HU Fei^1a,WEN Chang^1a,XIE Kai^1b,HE Jianbiao²

1a.School of Computer Science; 1b.School of Electronic Information,Yangtze University,Jingzhou,Hubei 434023,China; 2.College of Information Science and Engineering,Central South University,Changsha 410083,China

Received:2018-03-12 Online:2019-05-15 Published:2019-05-15

摘要/Abstract

摘要：

为解决身份认证过程中可能会出现的打印攻击、视频重播攻击等安全问题,提出一种多线索融合人脸活体检测方法。利用金字塔LK光流追踪视频帧并将其进行剪切波变换,以获取图像质量特征,通过卷积神经网络对数据集进行网络微调,得到真假活体。在Print-attack数据库和CISIA数据库上进行实验,结果表明,与LFDNet方法相比,该方法具有较高的人脸活体检测准确率,可用于抵制欺骗攻击。

关键词: 卷积神经网络, 微调策略, 剪切波变换, 金字塔LK光流, 人脸活体检测

Abstract:

To solve the security problems such as print attacks and video replay attacks that may occur during the authentication process,a multi-cue fusion method for detecting human faces is proposed.The pyramid Lucas-Kanade(LK) optical flow is used to track the video frame,and it is subjected to shear wave transform to obtain image quality features.The Convolutional Neural Network(CNN) is used to fine tune the data set to obtain true and false liveness bodies.Experiments on the Print-attack database and the CISIA database show that compared with the LFDNet method,this method has higher face liveness detection accuracy and can be applied to spoofing attacks resistance.

Key words: Convolutional Neural Network(CNN), fine-tuning strategy, shear wave transform, pyramid Lucas-Kanade(LK) optical flow, face liveness detection

中图分类号:

TP391.4

胡斐,文畅,谢凯,贺建飚. 基于微调策略的多线索融合人脸活体检测[J]. 计算机工程, 2019, 45(5): 256-260.

HU Fei,WEN Chang,XIE Kai,HE Jianbiao. Multi-cue fusion face liveness detection based on fine-tuning strategy[J]. Computer Engineering, 2019, 45(5): 256-260.

http://www.ecice06.com/CN/Y2019/V45/I5/256

参考文献

［1］PAN Gang,SUN Lin,WU Zhaohui,et al.Eyeblink-based anti-spoofing in face recognition from a generic webcamera［C］//Proceedings of the 11th International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2007:1-8.
［2］KOLLREIDER K,FRONTHALER H,FARAJ M I,et al.Real-time face detection and motion analysis with application in “liveness” assessment［J］.IEEE Transactions on Information Forensics and Security,2007,2(3):548-558
［3］BAO Wei,LI Hong,LI Nan,et al.A liveness detection method for face recognition based on optical flow field［C］//Proceedings of International Conference on Image Analysis and Signal Processing.Washington D.C.,USA:IEEE Press,2009:233-236.
［4］任安虎,刘贝.基于Adaboost的人脸识别眨眼检测［J］.计算机与数字工程,2016,44(3):521-524.
［5］SINGH A K,JOSHI P,NANDI G C.Face recognition with liveness detection using eye and mouth movement［C］//Proceedings of International Conference on Signal Propagation and Computer Technology.Washington D.C.,USA:IEEE Press,2014:592-597.
［6］ANJOS A,CHAKKA M M,MARCEL S.Motion-based counter-measures to photo attacks in face recognition［J］.IET Biometrics,2014,3(3):147-158
［7］MAATTA J,HADID A,PIETIKAINEN M.Face spoofing detection from single images using micro-texture analysis［C］//Proceedings of International Joint Conference on Biometrics.Washington D.C.,USA:IEEE Press,2011:1-7.
［8］方天红,陈庆虎,廖海斌,等.融合纹理与形状的人脸加权特征［J］.武汉大学学报(信息科学版),2015,40(3):321-326,340.
［9］KIM Y,NA J,YOON S,et al.Masked fake face detection using radiance measurements［J］.Journal of the Optical Society of America A-Optics Image Science and Vision,2009,26(4):760-766.
［10］ZHANG Zhiwei,YI Dong,LEI Zhen,et al.Face liveness detection by learning multispectral reflectance distributions［C］//Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition and Workshops.Washington D.C.,USA:IEEE Press,2011:436-441.
［11］EASLEY G,LABATE D,LIM W Q.Sparse directional image representations using the discrete shearlet transform［J］.Applied and Computational Harmonic Analysis,2008,25(1):25-46.
［12］许晓.基于深度学习的活体人脸检测算法研究［D］.北京:北京工业大学,2016.
［13］KUTYNIOKG,LABATE D.Shearlets:multiscale analysis for multivariate data［M］.［S.l.］:Birkhuser Basel,2012.
［14］CHINGOVSKA I,ANJOS A,MARCEL S.On the effectiveness of local binary patterns in face anti-spoofing［C］//Proceedings of International Conference of Biometrics Special Interest Group.Washington D.C.,USA:IEEE Press,2012:183-194.
［15］ZHANG Zhiwei,YAN Junjie,LIU Sifei,et al.A face antispoofing database with diverse attacks［C］//Proceedings of the 5th IAPR International Conference on Biometrics.Washington D.C.,USA:IEEE Press,2012:26-31.

[1]	曹坪, 杨怀志, 薄一军, 尤嘉, 张淳杰, 李丹勇. 面向低质量裂缝图像的多知识蒸馏分类[J]. 计算机工程, 2023, 49(7): 204-213.
[2]	白明昌. 基于折叠路径聚合的属性网络节点嵌入方法[J]. 计算机工程, 2023, 49(7): 76-84.
[3]	代祖华, 刘园园, 狄世龙. 语义增强的图神经网络方面级文本情感分析[J]. 计算机工程, 2023, 49(6): 71-80.
[4]	沈学利, 田桂源, 姜彦吉, 马琳琳. 基于双阶段Conv-Transformer的时频域语音增强算法[J]. 计算机工程, 2023, 49(6): 123-130.
[5]	丁子轩, 俞雷, 张娟, 李想, 王新宇. 基于深度残差自适应注意力网络的图像超分辨率重建[J]. 计算机工程, 2023, 49(5): 231-238.
[6]	陈治旭, 靳雁霞, 芦烨, 杨晶, 刘亚变, 史志儒. 基于子图卷积神经网络的多精度服装建模方法[J]. 计算机工程, 2023, 49(4): 174-181.
[7]	徐康, 李霏, 姬东鸿. 结合依存图卷积与文本片段搜索的方面情感三元组抽取[J]. 计算机工程, 2023, 49(4): 61-67.
[8]	衡红军, 苗菁. 语义与句法信息加强的二元标记实体关系联合抽取[J]. 计算机工程, 2023, 49(4): 77-84.
[9]	钟宝荣, 吴夏灵. 基于高分辨率网络的轻量型人体姿态估计研究[J]. 计算机工程, 2023, 49(4): 226-232,239.
[10]	杨晶晶, 谢海燕, 薛妮妮, 张傲明. 基于双通道残差网络的水下图像去噪研究[J]. 计算机工程, 2023, 49(4): 188-198.
[11]	刘晶晶, 黄浩. 引入非局部模块卷积神经网络的基频提取模型[J]. 计算机工程, 2023, 49(3): 128-133,160.
[12]	邹长龙, 安敬民, 李冠宇. 基于邻域聚合与CNN的知识图谱实体类型补全[J]. 计算机工程, 2023, 49(3): 134-141.
[13]	翟社平, 张宇航, 柏晓夏. 融合实体邻域信息的知识图谱嵌入负采样方法[J]. 计算机工程, 2023, 49(3): 95-104.
[14]	程小辉, 李钰, 康燕萍. 基于中间图特征提取的卷积网络双标准剪枝[J]. 计算机工程, 2023, 49(3): 105-112.
[15]	陈柏霖, 王天极, 任丽娜, 黄瑞章. 融合ELECTRA和文本局部信息的中文语法错误检测方法[J]. 计算机工程, 2023, 49(3): 304-311.

选择文件类型/文献管理软件名称

选择包含的内容

基于微调策略的多线索融合人脸活体检测

Multi-cue fusion face liveness detection based on fine-tuning strategy

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

基于微调策略的多线索融合人脸活体检测

Multi-cue fusion face liveness detection based on fine-tuning strategy

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价