通过深度对比学习揭示人内指纹相似性
Gabe Guo1, Aniv Ray1, Miles Izydorczak2
1Department of Computer Science, Columbia University, New York, NY 10027, USA.
Science advances
|January 12, 2024
概括
指纹生物识别依赖于一个未经证实的假设. 我们的研究显示,同一个人的不同手指的指纹之间存在很强的相似性,这挑战了现有的生物识别和法医方法.
科学领域:
- 生物识别和法医科学
- 机器学习和人工智能的人工智能
背景情况:
- 目前的指纹生物识别假定一个人的所有手指都是独一无二的.
- 这种假设在比较不同手指的指纹时限制了生物识别应用.
- 现有的法医科学依赖于传统的指纹匹配方法.
研究的目的:
- 调查同一个人不同手指的指纹的相似性.
- 挑战指纹生物识别中绝对独特性假设.
- 探索新的方法来提高法医调查的效率.
主要方法:
- 利用深层双胞胎神经网络提取指纹表示向量.
- 在个体内的所有手指对中分析了相似之处.
- 控制混因素,例如传感器模式.
主要成果:
- 超过99.99%的信心表明,来自同一人的不同手指的指纹表现出强烈的相似性.
- 识别了的方向,特别是在指纹中心附近,作为相似性的关键因素.
- 发现传统的基于细节的方法在很大程度上无法预测这种交叉指的相似性.
结论:
- 同一个人的手指之间完全不相似的假设可能是错误的.
- 深度学习方法可以识别显著的交叉指的相似性,改进生物识别和法医应用.
- 这一发现有可能显著提高法医调查效率.
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