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一个可扩展的ECG识别系统,基于局部敏感的哈希.

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    概括
    此摘要是机器生成的。

    这项研究引入了一个新的心电图 (ECG) 生物识别系统,使用本地敏感哈希 (LSH) 准确识别大型数据集中的个人. 该系统即使在许多注册对象的情况下也保持高准确度,提供可扩展的生物识别解决方案.

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    科学领域:

    • 生物识别信息 生物识别信息
    • 信号处理 信号处理
    • 机器学习 机器学习

    背景情况:

    • 心电图 (ECG) 是独特的,动态的生物识别特征.
    • 现有的心电图识别系统在大量的入学人数方面遇到了困难.
    • 在基于ECG的生物识别技术中,可扩展性是一个挑战.

    研究的目的:

    • 提出一个可扩展的ECG生物识别系统.
    • 为了在众多注册人中保持高的识别准确性.
    • 为了利用局部敏感哈希 (LSH) 进行高效的识别.

    主要方法:

    • 开发了一个使用局部敏感哈希 (LSH) 的ECG生物识别系统.
    • 整合了一个内核密度估计器,以排除未注册的主题.
    • 通过使用PTB数据集中的285名受试者的ECG来评估系统.

    主要成果:

    • 实现了99%的识别率 (IR).
    • 实现了4%的相同错误率 (EER).
    • 经过Nen/Nid = 15/3.3的证明有效性

    结论:

    • 提出的基于LSH的ECG生物识别系统对于大规模识别是有效的.
    • 该系统为ECG生物识别提供了一个可扩展的解决方案.
    • 核密度估计有助于强大的受试者排除.