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基于PPG的实时生物识别:通过2D语法矩阵和深度学习模型推进安全.

Ali Cherry1,2, Aya Nasser1, Wassim Salameh3

  • 1Department of Biomedical Engineering, Lebanese International University, Beirut P.O. Box 146404, Lebanon.

Sensors (Basel, Switzerland)
|January 11, 2025
PubMed
概括

光电显微镜 (PPG) 信号提供了一种安全的,非侵入性的生物识别身份验证方法. 这项研究展示了一种使用PPG信号和AI进行高度准确的生命检测的新系统,大大提高了对伪造攻击的安全性.

关键词:
格拉姆矩阵转换转换生物识别安全 生物识别安全这是分类分类的分类.深度学习是一种深度学习.活力检测 检测 生命力检测摄影电流 (PPG) 信号的信号.实时预测 实时预测这是一个二维格式的格式.

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科学领域:

  • 生物识别和安全工程
  • 信号处理和机器学习

背景情况:

  • 活力检测对于生物识别系统的安全性至关重要,防止伪造.
  • 传统方法面临挑战,需要先进的,防伪的替代方案.
  • 光电显微镜 (PPG) 信号是一个有希望的,非侵入性的生物识别方式.

研究的目的:

  • 评估PPG信号在生物识别身份验证中的生命检测效果.
  • 开发和验证使用PPG实时生物识别系统的强大系统.
  • 通过利用 PPG 信号固有的防伪能力来提高安全性.

主要方法:

  • 采集了40名受试者的PPG信号,使用定制采集系统.
  • 通过格拉姆矩阵转换将PPG信号转换为2D表示.
  • 使用EfficientNetV2 B0模型与LSTM网络进行分析和认证.

主要成果:

  • 在生物识别身份验证测试套件中获得了99%的准确性.
  • 证明了高精度,回忆和F1分数,表明了强大的性能.
  • 在实时识别场景中成功验证了模型.

结论:

  • PPG信号是一种成本高效且高度防伪的生物识别源.
  • 开发的EfficientNetV2 B0-LSTM模型为下一代生物识别系统提供了卓越的解决方案.
  • 该系统为生物识别提供了更高的安全性和有效性.