《计算机应用研究》|Application Research of Computers

基于选择性搜索和卷积神经网络的人脸检测

Face detection based on selective search and Gabor optimizing convolutional neural network

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作者 吴素雯,战荫伟
机构 广东工业大学 计算机学院,广州 510006
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2017)09-2854-04
DOI 10.3969/j.issn.1001-3695.2017.09.064
摘要 针对复杂背景下存在的光照变化及多姿态的人脸检测问题,提出一种基于Gabor优化的卷积神经网络与选择性搜索策略相结合的算法进行人脸检测。首先采用选择性搜索策略检测出图像中可能存在人脸的目标候选窗口,将候选窗口中的图像子块作为训练好的改进卷积神经网络的输入,经过一系列卷积和池化操作后,提取窗口图像的特征信息并进行分类,确认候选窗口中是否包含人脸。算法在LFW人脸数据库上取得了较高的检测率及检测速度。实验结果表明,融合Gabor特征的卷积神经网络用于人脸检测时可避免传统手工提取特征造成的不确定性,具有更好的泛化能力及鲁棒性。
关键词 卷积神经网络;选择性搜索;人脸检测;Gabor核
基金项目 广东省科技计划资助项目(2014B040401012)
本文URL http://www.arocmag.com/article/01-2017-09-064.html
英文标题 Face detection based on selective search and Gabor optimizing convolutional neural network
作者英文名 Wu Suwen, Zhan Yinwei
机构英文名 SchoolofComputer,GuangdongUniversityofTechnology,Guangzhou510006,China
英文摘要 To solve the problem of detecting faces with large variances on pose and illumination under complex background, this paper presented a robust and fast algorithm which combined selective search strategy with Gabor optimizing convolutional neural network. Selective search strategy selected the candidate regions. Image warping computed a fixed-size convolutional neural network input from each region proposal. After a series of convolution and pooling operations, convolutional neural network extracted features of candidate regions to confirm whether candidate regions contained faces or not. Experiments on LFW database show that Gabor optimizing convolutional neural network can avoid the uncertainty of traditional feature extraction and has better generalization and robustness.
英文关键词 convolutional neural network; selective search; face detection; Gabor kernel
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收稿日期 2016/6/21
修回日期 2016/7/28
页码 2854-2857,2876
中图分类号 TP391.41
文献标志码 A