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。
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.
引文
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