摘要
人脸活体检测是人脸识别过程中的一个重要环节,对于身份验证的安全性尤为重要。针对人脸识别过程存在照片、视频、面具、头套、头模等欺骗手段,通过Intel Realsense相机采集人脸RGB图和深度图信息,并在MobileNetV3的基础上提出了特征融合的轻量级活体检测网络,将深度图与RGB图的特征融合起来并且进行端到端的训练。而为了解决深度学习中参数量较大以及网络尾部对于权重区域的区分的问题,提出在网络尾部采用Streaming Module以减少网络参数量并且对权重区域进行区分。在CASIA-SURF数据集以及所制作的CQNU-LN数据集上进行仿真实验,结果表明所提方法在两个数据集上均于TPR@FPR=10E-4的级别上达到了95%的精度,相较对比方法中精度最高的ShuffleNet分别提高了0.1%和0.05%;在所制作的CQNU-3Dmask数据集上,所提方法于TPR@FPR=10E-4的级别达到了95.2%的精度,比仅训练RGB图或仅训练深度图的方法分别提升了0.9%和6.5%,并且,模型的参数文件的大小仅为1.8 MB,每秒浮点数运算量(FLOPs)仅为1.5×10^6。该方法能够在实际应用中对提取到的人脸进行准确的实时检测。
Face liveness detection is an important part of the face recognition process,and is particularly important for the security of identity verification.In view of the cheating methods such as photo,video,mask,hood and head model in the face recognition process,the RGB map and depth map information of the face was collected by the Intel Realsense camera,and a lightweight liveness detection of feature fusion was proposed based on MobileNetV3 to fuse the features of the depth map and the RGB map together and perform the end-to-end training.To solve the problem of large parameter quantity in deep learning and the distinction of the weight areas by the network tail,the method of using Streaming Module at the network tail was proposed to reduce the quantity of network parameters and distinguish weight regions.Simulation experiments were performed on CASIA-SURF dataset and the constructed CQNU-LN dataset.The results show that,on both datasets,the proposed method achieves an accuracy of 95%with TPR@FPR=10 E-4,which is increased by 0.1%and 0.05%respectively compared to ShuffleNet with the highest accuracy in the comparison methods.The accuracy of the proposed method reaches an accuracy of 95.2%with TPR@FPR=10 E-4 on the constructed CQNU-3 Dmask dataset,which is improved by 0.9%and 6.5%respectively compared to those of the method training RGB maps only and the method training depth maps only.In addition,the proposed model has the parameter quantity of only 1.8 MB and FLoating-point Operations Per second(FLOPs)of only 1.5×10^6.The proposed method can perform accurate and real-time liveness detection on the extracted face target in practical applications.
作者
皮家甜
杨杰之
杨琳希
彭明杰
邓雄
赵立军
唐万梅
吴至友
PI Jiatian;YANG Jiezhi;YANG Linxi;PENG Mingjie;DENG Xiong;ZHAO Lijun;TANG Wanmei;WU Zhiyou(School of Computer and Information Science,Chongqing Normal University,Chongqing 401331,China;Chongqing Digital Agriculture Service Engineering Technology Research Center(Chongqing Normal University),Chongqing 401331,China;Chongqing Key Laboratory of Intelligent Finance and Big Data Analysis(Chongqing Normal University),Chongqing 401331,China;School of Mathematical Sciences,Chongqing Normal University,Chongqing 401331,China)
出处
《计算机应用》
CSCD
北大核心
2020年第12期3658-3665,共8页
journal of Computer Applications
基金
国家自然科学基金资助项目(11971083)
重庆市教委科技项目青年项目(KJQN201800521)
重庆市基础研究与前沿探索项目(cstc2018jcyjAX0470)
重庆师范大学2019年研究生科研创新项目(YKC19014)。
关键词
计算机视觉
卷积神经网络
人脸活体检测
多模态特征融合
轻量级网络
computer vision
convolutional neural network
face liveness detection
multi-modal feature fusion
lightweight network
