| 基于优化LeNet-5的近红外图像中的静默活体人脸检测 |
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| 引用本文： | 黄俊,张娜娜,章惠. 基于优化LeNet-5的近红外图像中的静默活体人脸检测[J]. 红外技术, 2021, 43(9): 845-851 |
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| 作者姓名： | 黄俊 张娜娜 章惠 |
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| 作者单位： | 上海海洋大学信息学院,上海 201306;上海建桥学院信息技术学院,上海 201306 |
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| 基金项目： | 上海市教育委员会“晨光计划”基金项目AASH1702 |
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| 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...
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| 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 |
| 收稿时间： | 2020-12-01 |
Silent Live Face Detection in Near-Infrared Images Based on Optimized LeNet-5 |
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| Affiliation: | 1.College of Information Technology, Shanghai Ocean University, Shanghai 201306, China2.College of Information Technology, Shanghai Jian Qiao University, Shanghai 201306, China |
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| Abstract: | 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. |
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