基于高维几何仿生模式识别的人脸识别

Youzheng Zhang, Hao Feng, Lijun Ding
{"title":"基于高维几何仿生模式识别的人脸识别","authors":"Youzheng Zhang, Hao Feng, Lijun Ding","doi":"10.1109/DBTA.2010.5658942","DOIUrl":null,"url":null,"abstract":"High-dimensional geometry is a vehicle to achieve biomimetic pattern recognition, which is a new model for recognition science and always to construct convex cell bodies for covering samples points in the space. By this way, in this paper, some k- dimension simplex were constructed for the purpose of covering the sample points of each class in the feature space, and then computed each the shortest distance between a sample waiting for recognizing and each simplex for face recognition. The method was applied for some famous face database, and the experimental results indicated that it was better than that of SVM,and indicated its effectiveness and feasibility.","PeriodicalId":320509,"journal":{"name":"2010 2nd International Workshop on Database Technology and Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Face Recognition Based on Biomimetic Pattern Recognition by High-Dimensional Geometry\",\"authors\":\"Youzheng Zhang, Hao Feng, Lijun Ding\",\"doi\":\"10.1109/DBTA.2010.5658942\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"High-dimensional geometry is a vehicle to achieve biomimetic pattern recognition, which is a new model for recognition science and always to construct convex cell bodies for covering samples points in the space. By this way, in this paper, some k- dimension simplex were constructed for the purpose of covering the sample points of each class in the feature space, and then computed each the shortest distance between a sample waiting for recognizing and each simplex for face recognition. The method was applied for some famous face database, and the experimental results indicated that it was better than that of SVM,and indicated its effectiveness and feasibility.\",\"PeriodicalId\":320509,\"journal\":{\"name\":\"2010 2nd International Workshop on Database Technology and Applications\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 2nd International Workshop on Database Technology and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DBTA.2010.5658942\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 2nd International Workshop on Database Technology and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DBTA.2010.5658942","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

高维几何是实现仿生模式识别的载体,仿生模式识别是识别科学的一个新模型,通常需要构造凸胞体来覆盖空间中的样本点。通过这种方法,本文通过构造k维单纯形来覆盖特征空间中每一类的样本点,然后计算待识别样本与人脸识别的每个单纯形之间的最短距离。将该方法应用于一些著名的人脸数据库,实验结果表明,该方法优于支持向量机,表明了该方法的有效性和可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Face Recognition Based on Biomimetic Pattern Recognition by High-Dimensional Geometry
High-dimensional geometry is a vehicle to achieve biomimetic pattern recognition, which is a new model for recognition science and always to construct convex cell bodies for covering samples points in the space. By this way, in this paper, some k- dimension simplex were constructed for the purpose of covering the sample points of each class in the feature space, and then computed each the shortest distance between a sample waiting for recognizing and each simplex for face recognition. The method was applied for some famous face database, and the experimental results indicated that it was better than that of SVM,and indicated its effectiveness and feasibility.
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
期刊最新文献
SRJA: Iceberg Join Processing in Wireless Sensor Networks A New Method of Selecting Pivot Features for Structural Correspondence Learning in Domain Adaptive Sentiment Analysis Apply of Data Ming Technology in CRM A New Like Fibonacci Sequence and Its Properties Multisensor Estimation Fusion for Wireless Networks with Mixed Data Delays
×
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