面具识别的创新混合方法,使用预训练面具检测和细分,强大的PCA和KNN分类器进行面具识别
Mohammed Eman1, Tarek M Mahmoud2,3, Mostafa M Ibrahim4
1Computer Science Department, Faculty of Computing and Artificial Intelligence, Beni Suef University, Beni-Suef 62511, Egypt.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
这项研究引入了一种使用深度学习和强大的主要组件分析 (RPCA) 的新的面具面部识别方法. 这种新的方法实现了97%的准确性,大大改善了面罩的识别.
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:
- 面罩在各种行业中越来越普遍,需要可靠的识别方法.
- 准确识别佩戴口罩的个人是安全和认证系统的关键挑战.
研究的目的:
- 开发一种新的,强大的面具面部识别方法.
- 为了提高在面具部分遮住面部的情况下识别个人的准确性和可靠性.
主要方法:
- 这是一种混合方法,结合了面具检测 (SSD-MobileNetV2),标志性和圆面部检测以及强大的主要组件分析 (RPCA) 的深度学习.
- 利用粒子群集优化 (PSO) 来微调K-最近邻近 (KNN) 特性和参数k以获得最佳性能.
- 使用RPCA有效地分离了封闭和非封闭的面部部件.
主要成果:
- 提出的方法实现了97%的识别率,超过了现有的最先进的技术.
- 在面具引起的面部遮方面表现出显著的强度.
- 深度学习,面部特征检测和RPCA的整合证明了面具面部识别的有效性.
结论:
- 开发的方法在面具面部识别技术方面取得了重大进展.
- 这种方法提供了高精度和可靠性,即使有显著的面部遮.
- 这项工作解决了在掩盖场景中有效验证身份的关键需求.
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