VASIR:先进虹膜识别技术的开源研究平台。

IF 1.5 4区 工程技术 Journal of Research of the National Institute of Standards and Technology Pub Date : 2013-04-23 eCollection Date: 2013-01-01 DOI:10.6028/jres.118.011
Yooyoung Lee, Ross J Micheals, James J Filliben, P Jonathon Phillips
{"title":"VASIR:先进虹膜识别技术的开源研究平台。","authors":"Yooyoung Lee,&nbsp;Ross J Micheals,&nbsp;James J Filliben,&nbsp;P Jonathon Phillips","doi":"10.6028/jres.118.011","DOIUrl":null,"url":null,"abstract":"<p><p>The performance of iris recognition systems is frequently affected by input image quality, which in turn is vulnerable to less-than-optimal conditions due to illuminations, environments, and subject characteristics (e.g., distance, movement, face/body visibility, blinking, etc.). VASIR (Video-based Automatic System for Iris Recognition) is a state-of-the-art NIST-developed iris recognition software platform designed to systematically address these vulnerabilities. We developed VASIR as a research tool that will not only provide a reference (to assess the relative performance of alternative algorithms) for the biometrics community, but will also advance (via this new emerging iris recognition paradigm) NIST's measurement mission. VASIR is designed to accommodate both ideal (e.g., classical still images) and less-than-ideal images (e.g., face-visible videos). VASIR has three primary modules: 1) Image Acquisition 2) Video Processing, and 3) Iris Recognition. Each module consists of several sub-components that have been optimized by use of rigorous orthogonal experiment design and analysis techniques. We evaluated VASIR performance using the MBGC (Multiple Biometric Grand Challenge) NIR (Near-Infrared) face-visible video dataset and the ICE (Iris Challenge Evaluation) 2005 still-based dataset. The results showed that even though VASIR was primarily developed and optimized for the less-constrained video case, it still achieved high verification rates for the traditional still-image case. For this reason, VASIR may be used as an effective baseline for the biometrics community to evaluate their algorithm performance, and thus serves as a valuable research platform. </p>","PeriodicalId":17039,"journal":{"name":"Journal of Research of the National Institute of Standards and Technology","volume":"118 ","pages":"218-59"},"PeriodicalIF":1.5000,"publicationDate":"2013-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.6028/jres.118.011","citationCount":"43","resultStr":"{\"title\":\"VASIR: An Open-Source Research Platform for Advanced Iris Recognition Technologies.\",\"authors\":\"Yooyoung Lee,&nbsp;Ross J Micheals,&nbsp;James J Filliben,&nbsp;P Jonathon Phillips\",\"doi\":\"10.6028/jres.118.011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The performance of iris recognition systems is frequently affected by input image quality, which in turn is vulnerable to less-than-optimal conditions due to illuminations, environments, and subject characteristics (e.g., distance, movement, face/body visibility, blinking, etc.). VASIR (Video-based Automatic System for Iris Recognition) is a state-of-the-art NIST-developed iris recognition software platform designed to systematically address these vulnerabilities. We developed VASIR as a research tool that will not only provide a reference (to assess the relative performance of alternative algorithms) for the biometrics community, but will also advance (via this new emerging iris recognition paradigm) NIST's measurement mission. VASIR is designed to accommodate both ideal (e.g., classical still images) and less-than-ideal images (e.g., face-visible videos). VASIR has three primary modules: 1) Image Acquisition 2) Video Processing, and 3) Iris Recognition. Each module consists of several sub-components that have been optimized by use of rigorous orthogonal experiment design and analysis techniques. We evaluated VASIR performance using the MBGC (Multiple Biometric Grand Challenge) NIR (Near-Infrared) face-visible video dataset and the ICE (Iris Challenge Evaluation) 2005 still-based dataset. The results showed that even though VASIR was primarily developed and optimized for the less-constrained video case, it still achieved high verification rates for the traditional still-image case. For this reason, VASIR may be used as an effective baseline for the biometrics community to evaluate their algorithm performance, and thus serves as a valuable research platform. </p>\",\"PeriodicalId\":17039,\"journal\":{\"name\":\"Journal of Research of the National Institute of Standards and Technology\",\"volume\":\"118 \",\"pages\":\"218-59\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2013-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.6028/jres.118.011\",\"citationCount\":\"43\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Research of the National Institute of Standards and Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.6028/jres.118.011\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2013/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Research of the National Institute of Standards and Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.6028/jres.118.011","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2013/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 43

摘要

虹膜识别系统的性能经常受到输入图像质量的影响,而输入图像质量又容易受到光照、环境和主体特征(例如距离、运动、面部/身体可见性、眨眼等)等因素的影响。VASIR(基于视频的虹膜识别自动系统)是nist开发的最先进的虹膜识别软件平台,旨在系统地解决这些漏洞。我们开发VASIR作为一种研究工具,不仅可以为生物识别社区提供参考(评估替代算法的相对性能),而且还可以推进(通过这种新兴的虹膜识别范式)NIST的测量任务。VASIR旨在适应理想(例如,经典的静止图像)和不太理想的图像(例如,脸部可见的视频)。VASIR有三个主要模块:1)图像采集2)视频处理3)虹膜识别。每个模块由几个子组件组成,这些子组件通过严格的正交实验设计和分析技术进行了优化。我们使用MBGC(多重生物识别大挑战)NIR(近红外)人脸可见视频数据集和ICE(虹膜挑战评估)2005静态数据集来评估VASIR的性能。结果表明,尽管VASIR主要针对约束较少的视频情况进行了开发和优化,但对于传统的静止图像情况,它仍然实现了很高的验证率。因此,VASIR可以作为生物识别界评估其算法性能的有效基线,从而作为一个有价值的研究平台。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

摘要图片

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
VASIR: An Open-Source Research Platform for Advanced Iris Recognition Technologies.

The performance of iris recognition systems is frequently affected by input image quality, which in turn is vulnerable to less-than-optimal conditions due to illuminations, environments, and subject characteristics (e.g., distance, movement, face/body visibility, blinking, etc.). VASIR (Video-based Automatic System for Iris Recognition) is a state-of-the-art NIST-developed iris recognition software platform designed to systematically address these vulnerabilities. We developed VASIR as a research tool that will not only provide a reference (to assess the relative performance of alternative algorithms) for the biometrics community, but will also advance (via this new emerging iris recognition paradigm) NIST's measurement mission. VASIR is designed to accommodate both ideal (e.g., classical still images) and less-than-ideal images (e.g., face-visible videos). VASIR has three primary modules: 1) Image Acquisition 2) Video Processing, and 3) Iris Recognition. Each module consists of several sub-components that have been optimized by use of rigorous orthogonal experiment design and analysis techniques. We evaluated VASIR performance using the MBGC (Multiple Biometric Grand Challenge) NIR (Near-Infrared) face-visible video dataset and the ICE (Iris Challenge Evaluation) 2005 still-based dataset. The results showed that even though VASIR was primarily developed and optimized for the less-constrained video case, it still achieved high verification rates for the traditional still-image case. For this reason, VASIR may be used as an effective baseline for the biometrics community to evaluate their algorithm performance, and thus serves as a valuable research platform.

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
33.30%
发文量
10
期刊介绍: The Journal of Research of the National Institute of Standards and Technology is the flagship publication of the National Institute of Standards and Technology. It has been published under various titles and forms since 1904, with its roots as Scientific Papers issued as the Bulletin of the Bureau of Standards. In 1928, the Scientific Papers were combined with Technologic Papers, which reported results of investigations of material and methods of testing. This new publication was titled the Bureau of Standards Journal of Research. The Journal of Research of NIST reports NIST research and development in metrology and related fields of physical science, engineering, applied mathematics, statistics, biotechnology, information technology.
期刊最新文献
Design Considerations for a Surface Disinfection Device Using Ultraviolet-C Light-Emitting Diodes. AbsorbanceQ: An App for Generating Absorbance Images from Brightfield Images. Broadband Dielectric Spectroscopy as a Potential Label-Free Method to Rapidly Verify Ultraviolet-C Radiation Disinfection. Perspectives and Recommendations Regarding Standards for Ultraviolet-C Whole-Room Disinfection in Healthcare. Atomic Model Structure of the NIST Monoclonal Antibody (NISTmAb) Reference Material.
×
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