虹膜图像的自动分割与识别:以双胞胎为例

2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN) Pub Date : 2017-03-16 DOI:10.1109/ICSCN.2017.8085415

C. N. Devi

{"title":"虹膜图像的自动分割与识别:以双胞胎为例","authors":"C. N. Devi","doi":"10.1109/ICSCN.2017.8085415","DOIUrl":null,"url":null,"abstract":"This paper describes an automatic approach for iris segmentation and recognition with focus on twins. The technique entails localizing and segmenting the iris, followed by iris normalization and obtaining distinctive features. Lastly, iris templates are matched to realize one to one and one to many recognition in twins. Further, effect of various template sizes on the accuracy and memory requirement are studied. To our knowledge, this is the first iris based authentication with special emphasis on twins.","PeriodicalId":383458,"journal":{"name":"2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Automatic segmentation and recognition of iris images: With special reference to twins\",\"authors\":\"C. N. Devi\",\"doi\":\"10.1109/ICSCN.2017.8085415\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes an automatic approach for iris segmentation and recognition with focus on twins. The technique entails localizing and segmenting the iris, followed by iris normalization and obtaining distinctive features. Lastly, iris templates are matched to realize one to one and one to many recognition in twins. Further, effect of various template sizes on the accuracy and memory requirement are studied. To our knowledge, this is the first iris based authentication with special emphasis on twins.\",\"PeriodicalId\":383458,\"journal\":{\"name\":\"2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN)\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-03-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSCN.2017.8085415\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSCN.2017.8085415","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 7

摘要

本文介绍了一种针对双胞胎的虹膜自动分割与识别方法。该技术需要对虹膜进行定位和分割，然后对虹膜进行归一化并获得显著特征。最后，对虹膜模板进行匹配，实现对双胞胎的一对一和一对多识别。此外，还研究了不同模板尺寸对模板精度和内存要求的影响。据我们所知，这是第一个特别强调双胞胎的基于虹膜的身份验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Automatic segmentation and recognition of iris images: With special reference to twins

This paper describes an automatic approach for iris segmentation and recognition with focus on twins. The technique entails localizing and segmenting the iris, followed by iris normalization and obtaining distinctive features. Lastly, iris templates are matched to realize one to one and one to many recognition in twins. Further, effect of various template sizes on the accuracy and memory requirement are studied. To our knowledge, this is the first iris based authentication with special emphasis on twins.

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

2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN)

自引率

0.00%

发文量