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基于大量数据集和精确的细节检测方法,对指纹细节的统计分析

Jiazhi Huang¹, Yunqi Tang¹, Kang Wang¹

¹School of Criminal Investigation, People's Public Security University of China, Beijing, China.

Journal of forensic sciences

|November 14, 2025

概括

人工智能提高了指纹识别的准确性. 先进的AI算法将细节检测提高到99.45%,为概率指纹模型提供科学基础.

科学领域:

法医科学法医科学法医科学
计算机科学计算机科学
生物识别信息生物识别信息

背景情况:

对错误定罪的担忧凸显了需要验证指纹证据的必要性.
现有的指纹识别方法需要进行科学审查和改进.
指纹识别的科学基础需要严格的统计分析.

研究的目的:

应用先进的AI算法进行指纹模式分类,姿势估计和细节检测.
统计分析六种微小类型的数量和空间分布.
为概率指纹识别模型量化微小细节的证据价值.

主要方法:

在620,211个指纹图像的大型数据集上利用了最先进的AI算法.
对指纹图案,细节类型及其空间分布进行统计分析.
探索了指纹图案,细节和特定手指之间的关系.

主要成果:

在细节检测方面获得了99.45%的平均准确性,这比之前的研究有所改善.
识别了旋转作为最常见的图案,与指和戒指相关;带有中指和小指的循环.
详细描述了六种微小类型的分布范围,并在核心和三角洲地区发现了明显的空间度.

结论:

这项研究为指纹识别提供了坚实的统计基础.
人工智能驱动的分析提高了指纹证据的客观性和科学严谨性.
这些发现支持开发概率指纹识别模型.

关键词:

标识标识标识标识标识一个小小的细节.对象检测检测对象检测对象检测模式分类模式分类模式分类构成估计估计的估计.统计统计统计统计统计

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IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...