加强社会安全:为公共人员识别和跟踪系统采用多式联网深度学习方法
D Yuvasini1, S Jegadeesan2, Shitharth Selvarajan3
1Department of Computer Science and Business Systems, Thiagarajar College of Engineering, Madurai, Tamilnadu, 625015, India. dyica@tce.edu.
Scientific reports
|October 13, 2024
概括
这项研究引入了一个智能人身份识别系统,使用步态,面部和虹膜识别来增强公共安全. 多式联动深度学习系统的准确率达到94%,改进了现有的威胁检测方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 生物识别信息 生物识别信息
背景情况:
- 公共空间面临着严重的安全威胁.
- 新兴技术为社会安全提供了潜在的解决方案.
- 智能人身份识别对于监控和安全至关重要.
研究的目的:
- 开发和评估公共空间的智能人身份识别系统.
- 通过多式联运识别方法提高安全性.
- 研究深度学习与公民身份识别系统的整合.
主要方法:
- 采用了多模式方法,结合了步态,面部和虹膜识别.
- 采用深层卷积神经网络 (DCNNs) 预先训练以进行个人预测.
- 在云服务器上实现了系统,并与Aadhar/SSN系统集成.
主要成果:
- 在公共空间识别个人的准确率达到了94%.
- 与现有的公共空间人身份识别系统相比,证明了更高的准确性.
- 通过系统集成,展示了改进的精度和立即救命援助的潜力.
结论:
- 拟议的多式联运安全识别系统为公共安全威胁提供了一个有希望的解决方案.
- 深度学习技术提高了人身识别的精度.
- 潜在的应用包括事故,盗窃和公共空间中的入侵者识别.
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