物联网解决方案和基于人工智能的面具面部和面部识别框架,以打击COVID-19大流行
Jamal Al-Nabulsi1, Nidal Turab2, Hamza Abu Owida1
1Medical Engineering Department, Faculty of Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
这项研究审查了面具面部识别的算法和系统,这对于流行病期间的公共卫生至关重要. 它强调了深度学习和机器学习的进步,用于识别佩戴口罩的人.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 由于COVID-19疫情造成了全球卫生紧急情况,需要采取诸如面具使用等公共卫生措施.
- 世界卫生组织 (WHO) 建议使用口罩来缓解疾病传播.
- 快速查口罩人员对于控制流行病至关重要.
研究的目的:
- 分析当前的研究环境以及面具面部识别算法和系统的未来方向.
- 讨论面部和面罩识别的重要性,应用和主要方法.
- 批判性地评估最近机器学习和面具面部识别的深度学习方面的进展.
主要方法:
- 审查最近的面部识别框架和系统.
- 对卷积神经网络 (CNN),深度学习 (DL),机器学习 (ML) 和移动网络技术的分析.
- 讨论基于物联网 (IoT) 的传感器来监控口罩的使用情况.
主要成果:
- 最近的科学工作证明了ML和DL工具的有效性,用于识别蒙面面孔.
- 与ML/DL算法集成的物联网传感器可以跟踪口罩合规性和警报当局.
- 对比分析为下一代人脸识别系统提供了洞察力.
结论:
- 面具面部识别是人工智能在公共卫生紧急情况中的关键应用.
- 需要继续进行研究,以应对挑战并改进下一代识别系统.
- 深度学习和物联网的进步为流行病管理提供了有希望的解决方案.
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