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富里埃同步压缩转换-ICA-EMD框架基于 EOG-生物识别通过自愿眼活动实现可持续和持续的身份验证

Kutlucan Gorur¹

¹Electrical and Electronics Engineering Department, Bandırma Onyedi Eylul University, 10250 Balıkesir, Turkey.

Biomimetics (Basel, Switzerland)

|August 25, 2023

概括

本研究介绍了一种基于电视图 (EOG) 的强大的生物识别认证系统,使用里埃同步挤压转换 (FSST),独立组件分析 (ICA) 和实证模式分解 (EMD). 该系统通过眼实现了高准确度的用户身份验证,提供了一个安全和可扩展的解决方案.

科学领域:

生物识别身份验证
信号处理信号处理
人与计算机的交互

背景情况:

有限的研究存在于基于电子眼镜 (EOG) 的生物识别,使用眼动或眼.
电气集团信号具有独特和永久的特征,适合个人识别.
在电子学习,电子银行和电子政府系统中越来越需要安全的在线身份验证至关重要.

研究的目的:

调查一个强大的FSST-ICA-EMD框架对EOG生物识别身份验证的性能.
通过使用自愿的眼动作来评估系统的有效性.
为数字应用提供可持续,连续和可靠的EOG生物识别身份验证方法.

主要方法:

使用了一个强大的框架,结合了富里埃同步挤压变换 (FSST),独立组件分析 (ICA) 和实证模式分解 (EMD).
使用FSST从EOG信号中准确地提取时间空间属性.
ICA用于分解多通道的EOG录音,EMD从背景EEG中分离了EOG信号.

主要成果:

在对比验证中实现了高识别精度 (ACC: ≥99.99%) 和曲线下面面积 (AUC: 0.99).
在七个人中,证明了高的真实接受率 (TAR: ≥98%) 和低的虚假接受率 (FAR: ≤3.33%).
这代表了FSST-ICA-EMD框架的首次探索,用于使用自愿眼的EOG-生物识别身份验证.

结论:

拟议的FSST-ICA-EMD框架提供了一个高度准确和强大的EOG-生物识别身份验证系统.
自发的眼动作可以有效地用于可靠的用户身份验证.
这种方法提供了一个可扩展和负担得起的解决方案,用于在数字环境中持续验证,包括电子学习.

关键词:

电子学习 (e-learning) 是一个电子学习系统.电磁磁指令 (EMD) 是一个电子指令.经济发展组织 - 生物识别在FSSTT中,我们可以看到.美国国际航空公司 (ICA) ICA ICA 集成的RNN深度模型.

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