基于几何局部化表面形状指标的三维人脸识别

Hyoungchul Shin, K. Sohn
{"title":"基于几何局部化表面形状指标的三维人脸识别","authors":"Hyoungchul Shin, K. Sohn","doi":"10.1109/ICARCV.2006.345192","DOIUrl":null,"url":null,"abstract":"This paper describes a pose invariant three-dimensional (3D) face recognition method using distinctive facial features. A face has its structural components like the eyes, nose and mouth. The positions and the shapes of the facial components are very important characteristics of a face. We extract invariant facial feature points on those components using the facial geometry from a normalized face data and calculate relative features using these feature points. We also calculate a shape index on each area of facial feature point to represent curvature characteristics of facial components. We perform recognition by using weighted distance matching, support vector machine (SVM) and independent component analysis (ICA)","PeriodicalId":415827,"journal":{"name":"2006 9th International Conference on Control, Automation, Robotics and Vision","volume":"96 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"3D Face Recognition with Geometrically Localized Surface Shape Indexes\",\"authors\":\"Hyoungchul Shin, K. Sohn\",\"doi\":\"10.1109/ICARCV.2006.345192\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes a pose invariant three-dimensional (3D) face recognition method using distinctive facial features. A face has its structural components like the eyes, nose and mouth. The positions and the shapes of the facial components are very important characteristics of a face. We extract invariant facial feature points on those components using the facial geometry from a normalized face data and calculate relative features using these feature points. We also calculate a shape index on each area of facial feature point to represent curvature characteristics of facial components. We perform recognition by using weighted distance matching, support vector machine (SVM) and independent component analysis (ICA)\",\"PeriodicalId\":415827,\"journal\":{\"name\":\"2006 9th International Conference on Control, Automation, Robotics and Vision\",\"volume\":\"96 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 9th International Conference on Control, Automation, Robotics and Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICARCV.2006.345192\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 9th International Conference on Control, Automation, Robotics and Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARCV.2006.345192","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13

摘要

本文提出了一种利用不同的人脸特征进行姿态不变的三维人脸识别方法。脸由眼睛、鼻子和嘴等结构组成。面部组成部分的位置和形状是面部非常重要的特征。我们利用归一化人脸数据中的面部几何特征提取这些分量上的不变特征点,并利用这些特征点计算相对特征。我们还在面部特征点的每个区域上计算形状指数来表示面部成分的曲率特征。我们使用加权距离匹配、支持向量机(SVM)和独立分量分析(ICA)来进行识别。
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
3D Face Recognition with Geometrically Localized Surface Shape Indexes
This paper describes a pose invariant three-dimensional (3D) face recognition method using distinctive facial features. A face has its structural components like the eyes, nose and mouth. The positions and the shapes of the facial components are very important characteristics of a face. We extract invariant facial feature points on those components using the facial geometry from a normalized face data and calculate relative features using these feature points. We also calculate a shape index on each area of facial feature point to represent curvature characteristics of facial components. We perform recognition by using weighted distance matching, support vector machine (SVM) and independent component analysis (ICA)
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
μ-Interaction Measure for Unstable Systems GestureCam: A Smart Camera for Gesture Recognition and Gesture-Controlled Web Navigation A HHMM-Based Approach for Robust Fall Detection Verifying a User in a Personal Face Space The Effect of Image Resolution on the Performance of a Face Recognition System
×
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