图像传感器的研究,以在智能城市背景下远程增强面部识别
José M Llauradó1, Francisco A Pujol2, David Tomás3
1Department of Computer Technology and Computation, University of Alicante, Alicante, Spain.
Scientific reports
|September 7, 2023
概括
本研究介绍了一种深度学习方法,用于智能城市安全中的远程面部识别. 一种新的数据集生成技术实现了超过99%的准确性,这对于有效的监控系统至关重要.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 安全系统安全系统
背景情况:
- 智能城市越来越依赖于先进的监测和监控.
- 人类活动识别 (HAR) 对于安全来说是一个复杂的,持续的挑战.
- 现有的数据集缺乏足够的数据来实现远程人脸识别.
研究的目的:
- 开发一种深度学习方法,用于准确的远距离人脸识别.
- 为培训和测试远程人脸识别模型创建可靠的数据集.
- 评估该方法在各种商业监控传感器中的性能.
主要方法:
- 一种用于远程识别面部的深度学习方法.
- 一种创新的方法来生成数据集,将距离,焦距和面部像素大小相关起来.
- 利用乔治亚理工面部和质量数据集用于距离面部数据集生成.
- 在用于监控摄像头的商业图像传感器上测试了该方法.
主要成果:
- 在多个传感器中实现了超过99%的平均准确性.
- 该系统可以确定给定传感器的最大有效识别距离.
- 证明了生成的数据集对于远程人脸识别任务的可行性.
结论:
- 拟议的深度学习方法为安全应用中的远程人脸识别提供了高准确度.
- 新型数据集生成技术解决了可用资源的关键差距.
- 这项技术对于加强智能城市环境中的安全至关重要.
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