基于卷积神经网络的手指静脉深度特征学习

Manjit Singh, S. Singla
{"title":"基于卷积神经网络的手指静脉深度特征学习","authors":"Manjit Singh, S. Singla","doi":"10.1145/3362752.3365193","DOIUrl":null,"url":null,"abstract":"The modern-day finger vein based human recognition techniques provide good performance, yet they are highly finger vein image quality dependent. To address this problem, a novel deep learning-based approach using convolution-neural-network (CNN) for finger vein identification has been introduced here. The prime objective of our work is to achieve a stable response with accurate performance keeping varying quality finger vein images in account. The proposed approach is tested on the considered publicly available dataset and reported experiment results show that with effective training and testing strategy high identification accuracy can be achieved.","PeriodicalId":430178,"journal":{"name":"Proceedings of the 2019 2nd International Conference on Electronics and Electrical Engineering Technology","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Convolutional Neural Network Based Deep Feature Learning for Finger-vein Identification\",\"authors\":\"Manjit Singh, S. Singla\",\"doi\":\"10.1145/3362752.3365193\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The modern-day finger vein based human recognition techniques provide good performance, yet they are highly finger vein image quality dependent. To address this problem, a novel deep learning-based approach using convolution-neural-network (CNN) for finger vein identification has been introduced here. The prime objective of our work is to achieve a stable response with accurate performance keeping varying quality finger vein images in account. The proposed approach is tested on the considered publicly available dataset and reported experiment results show that with effective training and testing strategy high identification accuracy can be achieved.\",\"PeriodicalId\":430178,\"journal\":{\"name\":\"Proceedings of the 2019 2nd International Conference on Electronics and Electrical Engineering Technology\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2019 2nd International Conference on Electronics and Electrical Engineering Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3362752.3365193\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 2nd International Conference on Electronics and Electrical Engineering Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3362752.3365193","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

目前基于指静脉的人体识别技术具有良好的性能,但对指静脉图像质量的依赖性较大。为了解决这个问题,本文介绍了一种新的基于深度学习的方法,该方法使用卷积神经网络(CNN)进行手指静脉识别。我们工作的主要目标是实现稳定的响应,准确的性能保持不同质量的手指静脉图像。实验结果表明,通过有效的训练和测试策略,该方法可以达到较高的识别精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Convolutional Neural Network Based Deep Feature Learning for Finger-vein Identification
The modern-day finger vein based human recognition techniques provide good performance, yet they are highly finger vein image quality dependent. To address this problem, a novel deep learning-based approach using convolution-neural-network (CNN) for finger vein identification has been introduced here. The prime objective of our work is to achieve a stable response with accurate performance keeping varying quality finger vein images in account. The proposed approach is tested on the considered publicly available dataset and reported experiment results show that with effective training and testing strategy high identification accuracy can be achieved.
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