Intelligent electronic lock with user-defined graphical key

Yunjiang Hou, Jun Cheng, Wei Feng, X. Zou
{"title":"Intelligent electronic lock with user-defined graphical key","authors":"Yunjiang Hou, Jun Cheng, Wei Feng, X. Zou","doi":"10.1109/ICINFA.2016.7832091","DOIUrl":null,"url":null,"abstract":"In this paper, we present a novel authentication method based on image feature matching to make intelligent lock key can be defined by user which will improve the security and ease-use of intelligent electronic lock. It is a new unlock scheme for intelligent lock that may replaces the text passwords and biological features which are typically used in intelligent lock system. In this method, an object or a digital picture displayed on a physical user-owned device such as a mobile phone can be defined as the intelligent electronic lock key. Firstly, obtain the image of object or digital picture that user defines as the key and the image of the user used for unlocking respectively. Secondly, highly distinctive optical features are extracted from two images and then matching with the features, afterwards the distance between two image's keypoints constitute a discriminant vector. Finally, a LINEAR Support Vector Machine (SVM) is used to classify the discriminant vector. Simulation and experimental results demonstrate the feasibility of proposed approach. The experimental results have obtained satisfactory effect.","PeriodicalId":389619,"journal":{"name":"2016 IEEE International Conference on Information and Automation (ICIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Information and Automation (ICIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICINFA.2016.7832091","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In this paper, we present a novel authentication method based on image feature matching to make intelligent lock key can be defined by user which will improve the security and ease-use of intelligent electronic lock. It is a new unlock scheme for intelligent lock that may replaces the text passwords and biological features which are typically used in intelligent lock system. In this method, an object or a digital picture displayed on a physical user-owned device such as a mobile phone can be defined as the intelligent electronic lock key. Firstly, obtain the image of object or digital picture that user defines as the key and the image of the user used for unlocking respectively. Secondly, highly distinctive optical features are extracted from two images and then matching with the features, afterwards the distance between two image's keypoints constitute a discriminant vector. Finally, a LINEAR Support Vector Machine (SVM) is used to classify the discriminant vector. Simulation and experimental results demonstrate the feasibility of proposed approach. The experimental results have obtained satisfactory effect.
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
带有用户自定义图形钥匙的智能电子锁
本文提出了一种新的基于图像特征匹配的认证方法,使用户可以自定义智能电子锁的密钥,从而提高了智能电子锁的安全性和易用性。它是一种新的智能锁解锁方案,可以取代智能锁系统中常用的文本密码和生物特征。在该方法中,在用户拥有的物理设备(如移动电话)上显示的对象或数字图像可以定义为智能电子锁钥匙。首先,分别获取用户定义为密钥的物体图像或数字图像和用于解锁的用户图像。其次,从两幅图像中提取高度显著的光学特征,然后与特征进行匹配,然后将两幅图像关键点之间的距离构成判别向量;最后,利用线性支持向量机(LINEAR Support Vector Machine, SVM)对判别向量进行分类。仿真和实验结果验证了该方法的可行性。实验结果取得了满意的效果。
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
Morphological component decomposition combined with compressed sensing for image compression An adaptive nonlinear iterative sliding mode controller based on heuristic critic algorithm Analysis of static and dynamic real-time precise point positioning and precision based on SSR correction High-performance motion control of an XY stage for complicated contours with BFC trajectory planning An improved swarm intelligence algorithm for multirate systems state estimation using the canonical state space models
×
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