Facial Recognition Based on Kernel PCA

Yanmei Wang, Yanzhu Zhang
{"title":"Facial Recognition Based on Kernel PCA","authors":"Yanmei Wang, Yanzhu Zhang","doi":"10.1109/ICINIS.2010.88","DOIUrl":null,"url":null,"abstract":"Feature extraction is among the most important problems in face recognition systems. In this paper, Kernel Principal Component Analysis (KPCA) has been used in feature extraction and face recognition. By the use of integral kernel function, one can efficiently compute principal components in high dimensional feature spaces, related to input space by some nonlinear map. Polynomial kernel was used. The experimental results demonstrate that the KPCA is not only good at dimensional reduction, but also available to get better performance than conventional PCA. The highest rate is 90%.","PeriodicalId":319379,"journal":{"name":"2010 Third International Conference on Intelligent Networks and Intelligent Systems","volume":"124 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Third International Conference on Intelligent Networks and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICINIS.2010.88","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11

Abstract

Feature extraction is among the most important problems in face recognition systems. In this paper, Kernel Principal Component Analysis (KPCA) has been used in feature extraction and face recognition. By the use of integral kernel function, one can efficiently compute principal components in high dimensional feature spaces, related to input space by some nonlinear map. Polynomial kernel was used. The experimental results demonstrate that the KPCA is not only good at dimensional reduction, but also available to get better performance than conventional PCA. The highest rate is 90%.
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
基于核主成分分析的人脸识别
特征提取是人脸识别系统中的重要问题之一。本文将核主成分分析(KPCA)应用于特征提取和人脸识别。利用积分核函数可以有效地计算高维特征空间中的主成分,而高维特征空间与输入空间之间存在非线性映射关系。采用多项式核函数。实验结果表明,KPCA不仅具有较好的降维效果,而且可以获得比传统PCA更好的性能。最高的比率是90%。
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
Research of Command Automation System Survivability Assessment Model Fault Diagnosis of Metro Shield Machine Based on Rough Set and Neural Network A Framework for Ontology Integration and Evaluation Liaoning Province Economic Increasing Forecast and Analysis Based on ARMA Model Implementation of CAM System Integration Between STEP-NC and UG
×
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