利用人工智能进行人体识别的多模态生物识别技术

Boda Aruna, Dr. M Kezia Joseph
{"title":"利用人工智能进行人体识别的多模态生物识别技术","authors":"Boda Aruna, Dr. M Kezia Joseph","doi":"10.35940/ijese.a4278.1212123","DOIUrl":null,"url":null,"abstract":"Multimodal biometric systems combine multiple biometric modalities to enhance the accuracy and security of human identification. Instead of relying on a single biometric trait (such as fingerprint or face), these systems use a combination of different biometric characteristics to provide a more robust and reliable identification process. The key idea behind multimodal biometrics is that the fusion of diverse biometric data can overcome the limitations of individual modalities, resulting in higher accuracy and lower error rates.","PeriodicalId":275796,"journal":{"name":"International Journal of Emerging Science and Engineering","volume":" 44","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multimodal Biometrics for Human Identification usingArtificial Intelligence\",\"authors\":\"Boda Aruna, Dr. M Kezia Joseph\",\"doi\":\"10.35940/ijese.a4278.1212123\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multimodal biometric systems combine multiple biometric modalities to enhance the accuracy and security of human identification. Instead of relying on a single biometric trait (such as fingerprint or face), these systems use a combination of different biometric characteristics to provide a more robust and reliable identification process. The key idea behind multimodal biometrics is that the fusion of diverse biometric data can overcome the limitations of individual modalities, resulting in higher accuracy and lower error rates.\",\"PeriodicalId\":275796,\"journal\":{\"name\":\"International Journal of Emerging Science and Engineering\",\"volume\":\" 44\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Emerging Science and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.35940/ijese.a4278.1212123\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Emerging Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35940/ijese.a4278.1212123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

多模式生物识别系统结合了多种生物识别模式,以提高人体识别的准确性和安全性。这些系统不依赖单一的生物识别特征(如指纹或脸部),而是使用不同生物识别特征的组合,以提供更强大、更可靠的识别过程。多模态生物识别技术背后的关键理念是,融合不同的生物识别数据可以克服单个模态的局限性,从而提高准确性,降低错误率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimodal Biometrics for Human Identification usingArtificial Intelligence
Multimodal biometric systems combine multiple biometric modalities to enhance the accuracy and security of human identification. Instead of relying on a single biometric trait (such as fingerprint or face), these systems use a combination of different biometric characteristics to provide a more robust and reliable identification process. The key idea behind multimodal biometrics is that the fusion of diverse biometric data can overcome the limitations of individual modalities, resulting in higher accuracy and lower error rates.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信