摘要
传统人脸检测算法往往不能自动地从原始图像中提取有用的检测特征,而卷积神经网络可以轻易地提取高维度的特征信息,广泛用于图像处理领域。针对上述缺点,采用简单高效的深度学习Caffe框架并通过AlexNet网络训练,数据集为LFW人脸数据集,得出一个模型分类器,对原始图像数据进行图像金字塔变换,并通过前向传播得到特征图,反变换得出人脸坐标,采用非极大值抑制算法得出最优位置,最后达到一个二分类的人脸检测结果。该方法可以实现不同尺度的人脸检测,具有较高的精度,可用于构建人脸检测系统。
Traditional face detection algorithms often cannot extract useful detection features from the original image, and convolu-tional neural networks can easily extract high-dimensional feature information, which is widely used in image processing. In view of the above shortcomings, a simple and efficient deep learning Caffe framework is adopted and trained by AlexNet network. The data set is LFW face dataset, and a model classifier is obtained. Image pyramid transformation is performed on the original image data,and feature graph is obtained by forward propagation. The inverse transformation yields the face coordinates, uses the non-maximum suppression algorithm to obtain the optimal position, and finally reaches a two-class face detection result. The method can realize face detection with different scales and has high precision, and can be used to construct a face detection system.
作者
王静波
孟令军
Wang Jingbo;Meng Lingjun(National Key Laboratory for Electronic Measurement Technology,North University of China,Taiyuan 030051,China)
出处
《电子技术应用》
2020年第1期34-38,共5页
Application of Electronic Technique
关键词
人脸检测
卷积神经网络
深度学习
图像金字塔
非极大值抑制
face detection
convolutional neural network
deep learing
image pyramid
non-maximum suppression
