Li Kang, Han Yishi, Luo Yaping
School of Investigation, People's Public Security University of China, Beijing, China.
Forensic Science Department, Zhejiang Police College, Hangzhou, China.
Forensic Sci Res. 2024 Jan 11;9(1):owae002. doi: 10.1093/fsr/owae002. eCollection 2024 Mar.
Fingerprints with similar morphological characteristics but from different individuals can lead to errors in individual identification, especially when dealing with large databases containing millions of fingerprints. To address this issue and enhance the accuracy of similar fingerprint identification, the use of the likelihood ratio (LR) model for quantitative evaluation of fingerprint evidence has emerged as an effective research method. In this study, the LR fingerprint evidence evaluation model was established by using mathematical statistical methods, such as parameter estimation and hypothesis testing. This involved various steps, including database construction, scoring, fitting, calculation, and visual evaluation. Under the same-source conditions, the optimal parameter methods selected by different number of minutiae are gamma and Weibull distribution, while normal, Weibull, and lognormal distributions were the fitting parameters selected for minutiae configurations. The fitting parameters selected by different number of minutiae under different-source conditions are lognormal distribution, and the parameter methods selected for different minutiae configurations include Weibull, gamma, and lognormal distributions. The results of the LR model showed increased accuracy as the number of minutiae increased, indicating strong discriminative and corrective power. However, the accuracy of the LR evaluation based on different configurations was comparatively lower. In addition, the LR models with different numbers of minutiae outperformed those with different minutiae configurations. Our study shows that the use of LR models based on parametric methods is favoured in reducing the risk of fingerprint evidence misidentification, improving the quantitative assessment methods of fingerprint evidence, and promoting fingerprint identification from experience to science.
Likelihood ratio (LR) method based on parameter estimation was applied to scientific evaluation of fingerprint evidence with excellent discriminatory and calibration capabilities.Both the number of minutiae and configuration of minutiae have significant effects on the score-based LR method.Fingerprints from the same source contain many different patterns of deformation.Databases containing 10 million fingerprints from different sources have been used for building the LR model.
具有相似形态特征但来自不同个体的指纹可能导致个体识别错误，尤其是在处理包含数百万指纹的大型数据库时。为了解决这个问题并提高相似指纹识别的准确性，使用似然比（LR）模型对指纹证据进行定量评估已成为一种有效的研究方法。在本研究中，通过参数估计和假设检验等数理统计方法建立了LR指纹证据评估模型。这涉及多个步骤，包括数据库构建、评分、拟合、计算和可视化评估。在同源条件下，不同数量细节特征选择的最优参数方法是伽马分布和威布尔分布，而正态分布、威布尔分布和对数正态分布是为细节特征配置选择的拟合参数。在不同源条件下，不同数量细节特征选择的拟合参数是对数正态分布，为不同细节特征配置选择的参数方法包括威布尔分布、伽马分布和对数正态分布。LR模型的结果表明，随着细节特征数量的增加，准确性提高，表明具有很强的区分和校正能力。然而，基于不同配置的LR评估准确性相对较低。此外，具有不同数量细节特征的LR模型优于具有不同细节特征配置的模型。我们的研究表明，使用基于参数方法的LR模型有利于降低指纹证据误识别的风险，改进指纹证据的定量评估方法，并推动指纹识别从经验走向科学。
基于参数估计的似然比（LR）方法应用于指纹证据的科学评估，具有出色的区分和校准能力。细节特征的数量和配置对基于分数的LR方法都有显著影响。来自同一来源的指纹包含许多不同的变形模式。已使用包含来自不同来源的1000万指纹的数据库来构建LR模型。
