两层级联卷积神经网络的人脸检测  

Two-layer cascaded convolutional neural network for face detection

作　　者：张海涛[1] 李美霖 董帅含 Zhang Haitao;Li Meilin;Dong Shuaihan(College of Software,Liaoning Technical University,Huludao 125105,China)

机构地区：[1]辽宁工程技术大学软件学院,葫芦岛125105

出　　处：《中国图象图形学报》2019年第2期203-214,共12页Journal of Image and Graphics

基　　金：中国人民解放军总装备部装备预研基金项目(61421070101162107002);辽宁省自然科学基金面上项目(20170540426)~~

摘　　要：目的传统人脸检测方法因人脸多姿态变化和人脸面部特征不完整等问题,导致检测效果不佳。为解决上述问题,提出一种两层级联卷积神经网络(TC_CNN)人脸检测方法。方法首先,构建两层卷积神经网络模型,利用前端卷积神经网络模型对人脸图像进行特征粗略提取,再利用最大值池化方法对粗提取得到的人脸特征进行降维操作,输出多个疑似人脸窗口;其次,将前端粗提取得到的人脸窗口作为后端卷积神经网络模型的输入进行特征精细提取,并通过池化操作得到新的特征图;最后,通过全连接层判别输出最佳检测窗口,完成人脸检测全过程。结果实验选取FDDB人脸检测数据集中包含人脸多姿态变化以及人脸面部特征信息不完整等情况的图像进行测试,TC_CNN方法人脸检测率达到96. 39%,误检率低至3. 78%,相比当前流行方法在保证算法效率的同时检测率均有提高。结论两层级联卷积神经网络人脸检测方法能够在人脸多姿态变化和面部特征信息不完整等情况下实现精准检测,保证较高的检测率,有效降低误检率,方法具有较好的鲁棒性和泛化能力。Objective As an important part of face recognition,face detection has attracted considerable attention in comput-er vision and has been widely investigated.Face detection determines the location and size of human faces in an image.Traditional face detection methods are limited by face multi-pose changes and incomplete facial features,which lead to theirpoor detection effect.Modern face detectors can easily detect near-frontal faces.Recent research in this area has focused onthe uncontrolled face detection problem,where a number of factors,such as multi-pose changes and incomplete facial fea-tures,can lead to large visual variations in face appearance and can severely degrade the robustness of the face detector.Aconvolutional neural network can automatically select facial features,rapidly delete a large number of non-face backgroundinformation,and can achieve good face detection results.However,a single convolutional neural network should possessthree functions,namely,facial feature extraction,reduction of feature dimensions to decrease the computational complexi-ty,and feature classification,which result in complex network structure,limited detection speed,and overfitting of the net-work.To solve these problems,this study presents a face detection method of two-layer cascaded convolutional neural net-work( TC_ CNN).Method First,a two-layer convolutional neural network model is constructed.The first convolutionalneural network model is used to extract the features of the face image,and a max pooling method is adopted to reduce thedimension of those features in which multiple suspected face windows are outputted.Second,the face windows are used asthe inputs of the second convolutional neural network model for fine feature extraction,and a new feature map is obtained bypool operation.Finally,the best detection window is outputted through full connection layer discrimination.The face issuccessfully detected and the face window is returned when the result of discriminant classification is a face;otherwise,thenon-face

关 键 词：人脸检测 卷积神经网络 十折交叉验证 两层级联卷积神经网络 最大值池化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]