Face recognition based on LBPH and regression of Local Binary features

Gao Xiang, Zhu Qiuyu, Wang Hui, Chen Yan
{"title":"Face recognition based on LBPH and regression of Local Binary features","authors":"Gao Xiang, Zhu Qiuyu, Wang Hui, Chen Yan","doi":"10.1109/ICALIP.2016.7846668","DOIUrl":null,"url":null,"abstract":"This paper presents a system to recognize face by a variation of LBPH. We use a method of regression of local binary features to get the landmark of face image whose computational complexity is very low. We utilize these landmark points which can be trained to align the face, to extract the facial features. By calculating the Local Binary Patterns Histogram (LBPH) of these landmark points and its neighborhood pixels, we can extract effective facial feature to realize face recognition. This method can increase the calculating speed of LBPH and also can improve the recognition rate. Finally, we show the experimental results using this method to recognize face.","PeriodicalId":184170,"journal":{"name":"2016 International Conference on Audio, Language and Image Processing (ICALIP)","volume":"278 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Audio, Language and Image Processing (ICALIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICALIP.2016.7846668","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16

Abstract

This paper presents a system to recognize face by a variation of LBPH. We use a method of regression of local binary features to get the landmark of face image whose computational complexity is very low. We utilize these landmark points which can be trained to align the face, to extract the facial features. By calculating the Local Binary Patterns Histogram (LBPH) of these landmark points and its neighborhood pixels, we can extract effective facial feature to realize face recognition. This method can increase the calculating speed of LBPH and also can improve the recognition rate. Finally, we show the experimental results using this method to recognize face.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于LBPH和局部二值特征回归的人脸识别
本文提出了一种基于LBPH变化的人脸识别系统。我们采用局部二值特征回归的方法来获得计算复杂度很低的人脸图像地标。我们利用这些可以训练的地标点来对齐面部,提取面部特征。通过计算这些地标点及其邻域像素的局部二值模式直方图(LBPH),提取有效的人脸特征,实现人脸识别。该方法提高了LBPH的计算速度,提高了识别率。最后,给出了该方法用于人脸识别的实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Analysis of student activities trajectory and design of attendance management based on internet of things An RFID indoor positioning system by using Particle Swarm Optimization-based Artificial Neural Network Comparison of sparse-view CT image reconstruction algorithms Face recognition based on LBPH and regression of Local Binary features Research and application of dynamic and interactive data visualization based on D3
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1