使用优化曲线特征进行生物识别认证的高效热面部识别方法
Mona A S Ali1,2, Mohamed Meselhy Eltoukhy3,4, Fathimathul Rajeena P P1
1Computer Science Department, College of Computer Science and Information Technology, King Faisal University, Al Ahsa, Saudia Arabia.
PloS one
|June 26, 2023
概括
这项研究引入了一个优化的热面部识别系统,用于安全的身份验证. 拟议的方法使用曲线特征和灰狼优化实现了99.5%的准确性,提供了计算效率高的解决方案.
科学领域:
- 生物识别身份验证系统
- 计算机视觉和模式识别.
- 对于安全应用程序的机器学习.
背景情况:
- 生物识别技术越来越多地取代了传统的身份验证方法.
- 热面部识别提供独特的热特征,但需要分析特征选择对准确性和计算成本的影响.
- 资源有限的应用,如物联网 (IoT) 中的应用,需要高效的生物识别解决方案.
研究的目的:
- 提出和评估一个基于热面部的生物识别认证系统.
- 分析特征选择对识别精度和计算成本的影响.
- 为优化系统配置提供性能指标的全面评估.
主要方法:
- 一个五相系统:热面捕获,基于超像素的细分用于感兴趣区域 (ROI) 提取,波形和曲线变换用于特征提取.
- 使用生物灵感优化算法进行特征选择:灰狼优化器 (GWO),粒子优化 (PSO) 和遗传算法 (GA).
- 在Terravic面部IR数据集上使用随机森林 (RF),K-最近邻居 (KNN) 和天真贝叶斯 (NB) 分类器进行分类.
主要成果:
- 该系统使用GWO优化并与RF分类的曲线特征实现了高达99.5%的准确性.
- 这种性能比波形特征要好10%,使用特征数量减少了5%.
- 统计分析证实了拟议模型的意义,证明了具有最小特征集的优质热面认证.
结论:
- 拟议的热面部识别系统为生物识别身份验证提供了一个计算友好和高度准确的解决方案.
- 优化曲线特征与GWO和RF相结合,提供了一种强大的用户识别方法.
- 该系统的效率和高性能使其适用于资源有限的应用.
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