Face recognition at-a-distance based on sparse-stereo reconstruction

H. Rara, S. Elhabian, Asem M. Ali, Mike Miller, T. Starr, A. Farag
{"title":"Face recognition at-a-distance based on sparse-stereo reconstruction","authors":"H. Rara, S. Elhabian, Asem M. Ali, Mike Miller, T. Starr, A. Farag","doi":"10.1109/CVPRW.2009.5204301","DOIUrl":null,"url":null,"abstract":"We describe a framework for face recognition at a distance based on sparse-stereo reconstruction. We develop a 3D acquisition system that consists of two CCD stereo cameras mounted on pan-tilt units with adjustable baseline. We first detect the facial region and extract its landmark points, which are used to initialize an AAM mesh fitting algorithm. The fitted mesh vertices provide point correspondences between the left and right images of a stereo pair; stereo-based reconstruction is then used to infer the 3D information of the mesh vertices. We perform experiments regarding the use of different features extracted from these vertices for face recognition. The cumulative rank curves (CMC), which are generated using the proposed framework, confirms the feasibility of the proposed work for long distance recognition of human faces with respect to the state-of-the-art.","PeriodicalId":431981,"journal":{"name":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPRW.2009.5204301","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13

Abstract

We describe a framework for face recognition at a distance based on sparse-stereo reconstruction. We develop a 3D acquisition system that consists of two CCD stereo cameras mounted on pan-tilt units with adjustable baseline. We first detect the facial region and extract its landmark points, which are used to initialize an AAM mesh fitting algorithm. The fitted mesh vertices provide point correspondences between the left and right images of a stereo pair; stereo-based reconstruction is then used to infer the 3D information of the mesh vertices. We perform experiments regarding the use of different features extracted from these vertices for face recognition. The cumulative rank curves (CMC), which are generated using the proposed framework, confirms the feasibility of the proposed work for long distance recognition of human faces with respect to the state-of-the-art.
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
基于稀疏立体重建的远距离人脸识别
我们描述了一种基于稀疏立体重建的远距离人脸识别框架。我们开发了一个三维采集系统,该系统由两个CCD立体摄像机组成,安装在具有可调基线的平移倾斜单元上。我们首先检测面部区域并提取其地标点,用于初始化AAM网格拟合算法。拟合的网格顶点在立体图像对的左右图像之间提供点对应;然后使用基于立体的重建来推断网格顶点的三维信息。我们进行了关于使用从这些顶点提取的不同特征进行人脸识别的实验。使用所建议的框架生成的累积等级曲线(CMC)证实了所建议的远距离人脸识别工作相对于最新技术的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Robust real-time 3D modeling of static scenes using solely a Time-of-Flight sensor Image matching in large scale indoor environment Learning to segment using machine-learned penalized logistic models Modeling and exploiting the spatio-temporal facial action dependencies for robust spontaneous facial expression recognition Fuzzy statistical modeling of dynamic backgrounds for moving object detection in infrared videos
×
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