计算机工程 ›› 2019, Vol. 45 ›› Issue (8): 102-106,112.doi: 10.19678/j.issn.1000-3428.0052669

• 体系结构与软件技术 • 上一篇    下一篇

基于B/S架构的高并发虹膜识别系统

徐云涛1, 许武军1,2, 翟梦琳1,2   

  1. 1. 东华大学 信息科学与技术学院, 上海 200051;
    2. 数字化纺织服装技术教育部工程研究中心, 上海 200051
  • 收稿日期:2018-09-17 修回日期:2018-10-22 出版日期:2019-08-15 发布日期:2019-08-08
  • 作者简介:徐云涛(1995-),男,硕士研究生,主研方向为B/S架构、物联网;许武军,副教授;翟梦琳,讲师。
  • 基金项目:
    中央高校基本科研业务费专项资金(17D110410)。

High Concurrency Iris Recognition System Based on B/S Architecture

XU Yuntao1, XU Wujun1,2, ZHAI Menglin1,2   

  1. 1. College of Information Science and Technology, Donghua University, Shanghai 200051, China;
    2. Engineering Research Center of Digital Textile and Fashion Technology, Ministry of Education, Shanghai 200051, China
  • Received:2018-09-17 Revised:2018-10-22 Online:2019-08-15 Published:2019-08-08

摘要: 虹膜登录系统的扫描、处理和识别过程处于同一物理机下,会导致存在用户使用代价高和受益用户少等问题。为此,基于BIS架构构建一种虹膜登录系统模型。通过网络实现扫描端和处理端的分离,将虹膜扫描端置于浏览器中,处理端放在云服务器上。运用前端技术搭建用户注册、识别登录窗口,多用户同时通过浏览器调用优化的虹膜摄像头获取本人多张虹膜信息,发送给云服务器。在云服务器端使用Django、Nginx框架搭建部署虹膜识别系统,利用MySql存储用户信息,并且通过多线程、多进程技术基于CPU核心加快图像处理及并行搜索速度。实验结果表明,与普通虹膜识别系统相比,该模型百张图像处理速度提升60%,图像匹配搜索效率平均提升75%。

关键词: 虹膜识别, 网站登录, 云计算, 高并发性, Django框架, 并行搜索

Abstract: The scanning,processing and identification processes of existing iris login systems are under the same physical machine,which leads to the problems of high use cost and few benefited users.Therefore,an iris login system model is proposed.This model separates the scanning front-end and the processing back-end through the network,and places the iris scanning front-end in the browser and the processing back-end on the cloud server.The front-end technology is used to build user registration and identify login window.At the same time,multiple users can use the browser to call the optimized iris cameras to obtain their personal iris information and send it to the cloud server.On the cloud server end,Django and Nginx are used to deploy the iris algorithm application,and MySql is used to store user information.The CPU core is used to speed up image processing and parallel search through multi-threading and multi-process technologies.Experimental results show that this model improves the processing speed of one hundred images by 60% and the image matching search efficiency by 75% on average compared with the ordinary iris recognition process.

Key words: iris recognition, website login, cloud computing, high concurrency, Django frame, parallel search

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