增强生物识别身份验证的多模式方法
Nassim Ammour1, Yakoub Bazi1, Naif Alajlan1
1Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Journal of imaging
|September 27, 2023
概括
这项研究通过结合指纹和心电图 (ECG) 信号来增强生物识别安全性. 这种多式联络方式有效打击伪造攻击,提高了系统对假生物识别陈述的稳定性.
科学领域:
- 计算机科学 计算机科学
- 生物识别信息 生物识别信息
- 网络安全 网络安全
背景情况:
- 单模生物识别系统,通常使用指纹,容易受到呈现攻击 (伪造).
- 现有的系统缺乏强大的防御机制来抵御复杂的假生物识别陈述.
研究的目的:
- 开发一种增强的生物识别系统,能够抵御伪造攻击.
- 建议采用多式联络方式,将指纹和心电图 (ECG) 信号结合起来,以提高安全性.
主要方法:
- 设计了一个多式联网深度学习架构,集成指纹和心电图数据.
- 采用了数据效率高的变压器来提取特征.
- 利用堆叠和通道智能融合技术来实现特征向量组合.
主要成果:
- 拟议的多式联运系统证明了对呈现攻击的增强强性.
- 实验结果验证了将指纹和心电图模式结合在一起的有效性.
- 深度学习架构成功地将各种生物识别特征的特征融合在一起.
结论:
- 结合指纹和心电图信号提供了一个有希望的策略,以减轻生物识别伪造.
- 开发的多式联网深度学习系统显著提高了生物识别安全性.
- 与单模系统相比,这种方法提供了更可靠,更安全的识别方法.
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