现代电子技术2018，Vol.41Issue(9)：58-61,67,5.DOI:10.16652/j.issn.1004-373x.2018.09.013
一种基于融合深度卷积神经网络与度量学习的人脸识别方法
A face recognition method based on fusion of deep CNN and metric learning
摘要
Abstract
The current convolutional neural network(CNN)methods mostly take the increase of inter?class distance as the learning objective,but ignore the decrease of intra?class distance,which makes that the human face can′t be recognize accurately under some unrestricted conditions(such as posture and illumination). In order to eliminate the above problem,a face recogni?tion method based on deep CNN and metric learning method is proposed. A face feature extraction network based on multi?Incep?tion structure is presented to extract the feature with less parameters. A metric learning method based on joint loss is presented to perform the weighting joint for the softmax loss and center loss. The deep CNN and metric learning method are fused to reach the learning objective of inter?class distance increase and intra?class distance decrease. The experimental results indicate that the proposed method can extract the more discriminative facial features,and improve the more face recognition accuracy under unrestricted conditions than the Softmax loss method and methods fusing other metric learning modes.关键词
多Inception结构/深度卷积神经网络/度量学习方法/深度人脸识别/特征提取/损失函数融合Key words
multi⁃Inception structure/deep CNN/metric learning method/deep face recognition/feature extraction/loss function fusion分类
信息技术与安全科学引用本文复制引用
吕璐,蔡晓东,曾燕,梁晓曦..一种基于融合深度卷积神经网络与度量学习的人脸识别方法[J].现代电子技术,2018,41(9):58-61,67,5.基金项目
2016年广西科技计划项目(广西重点研发计划)(桂科AB16380264) （广西重点研发计划）
2014年国家科技支撑计划课题(2014BAK11B02)Project Supported by Guangxi Science and Technology Project in 2016(AB16380264),National Science and Technology Support Program in 2014(2014BAK11B02) （2014BAK11B02）
