基于卷积神经网络的人脸表情识别*

Lei Xu, M. Fei, Wenju Zhou, Aolei Yang
{"title":"基于卷积神经网络的人脸表情识别*","authors":"Lei Xu, M. Fei, Wenju Zhou, Aolei Yang","doi":"10.1109/ANZCC.2018.8606597","DOIUrl":null,"url":null,"abstract":"In order to reduce the complexity for extracting artificial features from the face image in facial expression recognition (FER), a novel method is proposed based on convolutional neural network (CNN) in this paper. This method first preprocesses the facial expression images, then some trainable convolution kernels are used to extract facial expression features, and second, the largest pooling layer is used to fewer dimensions, finally seven types of facial expressions are recognized with the Softmax classifier. The proposed method is verified with Kaggle facial expression recognition challenge dataset (FER2013). The experimental results show that the method has good recognition performance and generalization ability.","PeriodicalId":358801,"journal":{"name":"2018 Australian & New Zealand Control Conference (ANZCC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Face Expression Recognition Based on Convolutional Neural Network*\",\"authors\":\"Lei Xu, M. Fei, Wenju Zhou, Aolei Yang\",\"doi\":\"10.1109/ANZCC.2018.8606597\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to reduce the complexity for extracting artificial features from the face image in facial expression recognition (FER), a novel method is proposed based on convolutional neural network (CNN) in this paper. This method first preprocesses the facial expression images, then some trainable convolution kernels are used to extract facial expression features, and second, the largest pooling layer is used to fewer dimensions, finally seven types of facial expressions are recognized with the Softmax classifier. The proposed method is verified with Kaggle facial expression recognition challenge dataset (FER2013). The experimental results show that the method has good recognition performance and generalization ability.\",\"PeriodicalId\":358801,\"journal\":{\"name\":\"2018 Australian & New Zealand Control Conference (ANZCC)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Australian & New Zealand Control Conference (ANZCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ANZCC.2018.8606597\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Australian & New Zealand Control Conference (ANZCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ANZCC.2018.8606597","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14

摘要

为了降低面部表情识别中人脸图像人工特征提取的复杂度,提出了一种基于卷积神经网络(CNN)的人脸特征提取方法。该方法首先对面部表情图像进行预处理,然后利用一些可训练的卷积核提取面部表情特征,然后将最大池化层用于更少的维数,最后使用Softmax分类器对7种类型的面部表情进行识别。利用Kaggle面部表情识别挑战数据集(FER2013)对该方法进行了验证。实验结果表明,该方法具有良好的识别性能和泛化能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Face Expression Recognition Based on Convolutional Neural Network*
In order to reduce the complexity for extracting artificial features from the face image in facial expression recognition (FER), a novel method is proposed based on convolutional neural network (CNN) in this paper. This method first preprocesses the facial expression images, then some trainable convolution kernels are used to extract facial expression features, and second, the largest pooling layer is used to fewer dimensions, finally seven types of facial expressions are recognized with the Softmax classifier. The proposed method is verified with Kaggle facial expression recognition challenge dataset (FER2013). The experimental results show that the method has good recognition performance and generalization ability.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信