基于参数估计方法的指纹证据概率比率评估方法的研究
Kang Li1,2, Yishi Han2, Yaping Luo1
1School of Investigation, People's Public Security University of China, Beijing, China.
Forensic sciences research
|March 28, 2024
概括
概率比 (LR) 模型通过使用统计方法来提高指纹识别的准确性. 增加细节数量可以提高准确性,使指纹分析更科学,减少误识风险.
科学领域:
- 法医科学 法医科学 法医科学
- 生物识别信息 生物识别信息
- 统计建模 统计建模
背景情况:
- 从指纹的个人识别容易出现错误,特别是在大型数据库.
- 来自不同个体的指纹中类似的形态特征带来了识别挑战.
- 概率比率 (LR) 模型提供了一种定量方法来评估指纹证据.
研究的目的:
- 建立和评估一个概率比 (LR) 指纹证据评估模型.
- 为了提高从大型数据库中识别类似指纹的准确性.
- 将指纹识别从基于经验的实践转变为科学实践.
主要方法:
- 使用了包括参数估计和假设测试在内的数学统计方法.
- 进行了数据库构建,评分,拟合,计算和视觉评估.
- 最佳参数方法 (马,韦布尔,正常,逻辑正常分布) 根据细节数和配置在相同源和不同源条件下进行选择.
主要成果:
- LR模型表现出更高的精度,更高的细节数量,显示出强大的区分和纠正能力.
- 基于不同细节配置的LR评估准确性相对较低.
- 使用细节数量的LR模型优于基于细节配置的LR模型.
结论:
- 参数LR模型的使用是有利于减少指纹错误识别.
- 该研究改进了指纹证据的定量评估方法.
- LR模型促进了对指纹识别的更科学方法.
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