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SignEEG v1.0:生物识别系统的多式数据集与脑电图和手写签名

Ashish Ranjan Mishra1, Rakesh Kumar2, Vibha Gupta3

  • 1Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, UP, India. ash.cs.recs@gmail.com.

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概括

这项研究将手写签名与脑电图 (EEG) 脑活动相结合,以提高生物识别安全性. 多模式身份验证显著提高了系统的稳定性和防伪能力.

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科学领域:

  • 生物识别身份验证
  • 生理和行为生物识别.
  • 人与计算机的交互

背景情况:

  • 手写签名提供独特的行为生物识别,但容易被伪造.
  • 现有的生物识别系统需要加强对复杂攻击的安全性.
  • 非侵入性脑电图 (EEG) 提供了独特的,难以复制的生理数据.

研究的目的:

  • 增强基于签名的生物识别系统的稳定性和安全性.
  • 调查手写签名与EEG数据结合的协同效益.
  • 为生物识别研究引入一个新的多式联络数据集.

主要方法:

  • 开发了SignEEG v1.0数据集,包括来自70名受试者的EEG信号和手写签名.
  • 采集数据使用情感洞察用于EEG和Wacom One用于签名.
  • 在三个范式中获取数据:心理图像,运动图像和签名的物理执行.

主要成果:

  • 多模式整合EEG和签名显著提高了生物识别系统的稳定性.
  • 即使在有限的样本大小下,也实现了高可靠性.
  • 机器学习分类器证明了多模式方法的有效性.

结论:

  • 结合生理 (EEG) 和行为 (签名) 模式,可以提供卓越的生物识别安全性.
  • SignEEG v1.0 数据集和方法为未来的多式联络生物识别研究提供了坚实的基础.
  • 这种方法为安全的用户识别和验证提供了一个有希望的解决方案.