面向VTM的交互式活体检测算法

Interactive Liveness Detection Algorithm for VTM

为提升远程视频柜员机人脸识别登录系统的识别率和安全性,将改进的眨眼检测、背景检测和随机组合动作指令相结合,提出一种交互式活体检测算法。基于OpenCV级联分类器人脸检测和局部二值特征人脸对齐算法,结合坐标比例和眼球色素变化改进眨眼检测。利用背景检测和随机组合动作指令抵御动态视频攻击,加入图像质量检测与校正功能,使系统在弱光、歪斜等环境影响下对活体人脸检测有较好的检测效果。在活体人脸数据库CASIA-FASD和自建样本库上进行实验,结果表明,该算法识别率达到97.67%,与多光谱、卷积神经网络等检测算法相比性能有明显的提升。

In order to improve the recognition rate and security of the Video Teller Machine(VTM)face recognition login system,an interactive liveness detection algorithm that combines improved blink detection,background detection and random combined action instructions is proposed.Based on the OpenCV cascade classifier face detection and Local Binary Feature(LBF)face alignment algorithm,combining the coordinate proportion and the eye pigment change,the detection method is improved.Uses the background detection and the random combination action instruction to resist the dynamic video attack.Making use of the image quality detection and correction function,the system in weak light,skew and the other environmental condition has a good performance as well.Experiments are carried out on liveness face database CASIA-FASD and self-built sample library,the result shows that the recognition rate reaches 97.67%,which is obviously improved than multispectral,convolutional neural network,and the other existing detection algorithms.