基于面部识别和面罩检测的在线出席系统
Muhammad Haikal Mohd Kamil1, Norliza Zaini1, Lucyantie Mazalan1
1School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor Malaysia.
概括
这项研究引入了使用面部识别和口罩检测的在线出席系统. 该系统达到80%以上的准确性,提供了一个方便的,基于浏览器的出勤跟踪解决方案.
科学领域:
- 计算机科学 计算机科学
- 生物识别信息 生物识别信息
- 人工智能的人工智能
背景情况:
- 传统的出席系统往往是手动的,耗时的.
- 生物识别系统提供了更高的安全性和效率.
- 随着COVID-19的爆发,人们越来越需要非接触式解决方案,包括面具检测.
研究的目的:
- 开发一个在线出席系统,利用面部识别和面罩检测.
- 提供一个基于Web的界面,使其无需安装软件才能普及.
- 建立一个集中在线数据库,以有效管理出席数据.
主要方法:
- 用户支持矢量机 (SVM) 用于面部识别模型培训.
- 集成合成数据用于训练模型以检测面具的面部.
- 在Python中开发了服务器应用程序,并使用OpenCV进行图像处理.
- 在Web界面和数据库管理中使用PHP和MySQL.
主要成果:
- 使用预训练模型实现了大约81.8%的面部识别准确度.
- 实现了大约80%的面罩检测准确度.
- 证明了Python和PHP在线服务器处理的成功集成.
结论:
- 开发的系统提供了一个有效和可访问的在线出席解决方案.
- 面部识别和口罩检测的整合增强了出席追踪能力.
- 基于浏览器的界面可确保在各种终端上轻松使用.
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