基于优化LeNet-5的近红外图像中的静默活体人脸检测  

Silent Live Face Detection in Near-Infrared Images Based on Optimized LeNet-5

作　　者：黄俊 张娜娜[2] 章惠 HUANG Jun;ZHANG Nana;ZHANG Hui(College of Information Technology,Shanghai Ocean University,Shanghai 201306,China;College of Information Technology,Shanghai Jian Qiao University,Shanghai 201306,China)

机构地区：[1]上海海洋大学信息学院,上海201306 [2]上海建桥学院信息技术学院,上海201306

出　　处：《红外技术》2021年第9期845-851,共7页Infrared Technology

基　　金：上海市教育委员会“晨光计划”基金项目(AASH1702)。

摘　　要：针对当前交互式活体检测过程繁琐、用户体验性差的问题,提出了一种优化LeNet-5和近红外图像的静默活体检测方法。首先,采用近红外光摄像头构建了一个非活体数据集;其次,通过增大卷积核、增加卷积核数目、引入全局平均池化等方法对LeNet-5进行了优化,构建了一个深层卷积神经网络;最后,将近红外人脸图片输入到模型中实现活体静默活体检测。实验结果表明,所设计的模型在活体检测数据集上有较高的识别率,为99.95%,整个静默活体检测系统的运行速度约为18~22帧/s,在实际应用中鲁棒性较高。An improved method of silent liveness detection for LeNet-5 and near-infrared images is proposed to overcome the problem of the interactive liveness detection process and poor user experience. First, a face attack dataset was constructed using a near-infrared camera. Second, the LeNet-5 was optimized by increasing the number of convolution kernels and introducing global average pooling to construct a deep convolutional neural network. Finally, the near-infrared face image is input to the model to realize silent liveness detection. The experimental results show that the proposed model has a higher recognition rate for the liveness detection dataset, reaching 99.95%. The running speed of the silent liveness detection system is approximately 18-22 frames per second, which shows high robustness in practical applications.

关 键 词：LeNet-5 卷积神经网络 全局平均池化 近红外图像 静默活体检测 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]