摘要
人脸识别由于其便捷性和实用性而被广泛应用于各种门禁等场合,但容易受到多种形式的欺骗攻击(如照片攻击和视频攻击)。基于深度卷积神经网络(CNN)的活体检测虽然能够解决以上问题,但是却存在计算量大、对用户不友好以及难以部署于嵌入式系统等缺点,因此提出了一种实时的轻量级的人脸识别安全分类方法。通过将基于色彩纹理分析的人脸活体检测算法与人脸认证算法相融合,提出了一种在无需用户配合的单目摄像头场景下进行人脸活体检测和人脸验证的人脸识别算法。该算法支持实时人脸识别,具有更高的活体检测识别率与鲁棒性。为了验证该算法的性能,以CASIA-FASD和Replay-Attack作为实验的基准数据集,结果表明在活体检测中该算法的半错误率(HTER)为9.7%,等错误率(EER)为5.5%,而且在整个流程中处理1帧图像所需时间为0.12 s,验证了该算法的可行性和有效性。
Face recognition is widely applied in various practical conditions such as entrance guard due to its convenience and practicability.But it is vulnerable to various forms of spoofing attacks(such as photo attacks and video attacks).The liveness detection based on deep Convolution Neural Network(CNN)can solve the above problem but has disadvantages such as high calculation cost,unfriendly interaction mode and difficult deployment on embedded devices.Therefore,a real-time and lightweight security classification method of face recognition was proposed.The face liveness detection algorithm based on color and texture analysis was integrated with the face authentication algorithm,and a face recognition algorithm performing face liveness detection and face authentication in the situation of monocular camera without user cooperation was proposed.The proposed algorithm can support real-time face recognition and has higher liveness recognition rate and robustness.In order to validate the performance of the proposed algorithm,Chinese Academy of Sciences Institute of Automation-Face Anti-Spoofing Dataset(CASIA-FASD)and Replay-Attack dataset were utilized as the benchmark datasets of the experiment.The experimental results show that,in the liveness detection,the proposed algorithm has the Half Total Error Rate(HTER)of 9.7%and Equal Error Rate(EER)of 5.5%respectively,and has the time cost of0.12 s to process a frame of image in the whole process.The above results verify the feasibility and effectiveness of the proposed algorithm.
作者
陈放
刘晓瑞
杨明业
CHEN Fang;LIU Xiaorui;YANG Mingye(School of Automation,Qingdao University,Qingdao Shandong 266071,China;Institute for Future,Qingdao University,Qingdao Shandong 266071,China;Robotics Research Center,University of Electronic Science and Technology of China,Chengdu Sichuan 611731,China)
出处
《计算机应用》
CSCD
北大核心
2020年第12期3666-3672,共7页
journal of Computer Applications
基金
国家自然科学基金资助项目(U181320052)。
关键词
人脸识别
活体检测
轻量级神经网络
实时检测
安防系统
多任务卷积神经网络
色彩纹理分析
FaceNet
face recognition
liveness detection
lightweight neural network
real-time detection
security system
Multi-Task Convolutional Neural Network(MTCNN)
color and texture analysis
FaceNet
