Institut de Police Scientifique, School of Criminal Sciences, Building Batochime, University of Lausanne, CH-1015 Lausanne, Switzerland.
Forensic Sci Int. 2014 Feb;235:86-101. doi: 10.1016/j.forsciint.2013.12.003. Epub 2013 Dec 18.
In the context of the investigation of the use of automated fingerprint identification systems (AFIS) for the evaluation of fingerprint evidence, the current study presents investigations into the variability of scores from an AFIS system when fingermarks from a known donor are compared to fingerprints that are not from the same source. The ultimate goal is to propose a model, based on likelihood ratios, which allows the evaluation of mark-to-print comparisons. In particular, this model, through its use of AFIS technology, benefits from the possibility of using a large amount of data, as well as from an already built-in proximity measure, the AFIS score. More precisely, the numerator of the LR is obtained from scores issued from comparisons between impressions from the same source and showing the same minutia configuration. The denominator of the LR is obtained by extracting scores from comparisons of the questioned mark with a database of non-matching sources. This paper focuses solely on the assignment of the denominator of the LR. We refer to it by the generic term of between-finger variability. The issues addressed in this paper in relation to between-finger variability are the required sample size, the influence of the finger number and general pattern, as well as that of the number of minutiae included and their configuration on a given finger. Results show that reliable estimation of between-finger variability is feasible with 10,000 scores. These scores should come from the appropriate finger number/general pattern combination as defined by the mark. Furthermore, strategies of obtaining between-finger variability when these elements cannot be conclusively seen on the mark (and its position with respect to other marks for finger number) have been presented. These results immediately allow case-by-case estimation of the between-finger variability in an operational setting.
在调查自动指纹识别系统（AFIS）用于评估指纹证据的过程中，本研究调查了当比较已知来源的指纹与非同一来源的指纹时，AFIS 系统的评分变化。最终目标是提出一个基于似然比的模型，该模型允许对标记与打印的比较进行评估。特别是，该模型通过使用 AFIS 技术，受益于使用大量数据的可能性，以及已经内置的接近度测量，即 AFIS 评分。更确切地说，LR 的分子是从同一来源的印痕之间进行比较并显示相同的细节配置所获得的分数得出的。LR 的分母是通过从与非匹配来源数据库的疑问标记的比较中提取分数获得的。本文仅关注 LR 分母的分配。我们将其称为一般术语的手指间变异性。本文中与手指间变异性相关的问题是所需的样本量、手指数量和一般模式的影响，以及所包含的细节数量及其在给定手指上的配置的影响。结果表明，通过 10000 个分数可以可靠地估计手指间变异性。这些分数应该来自于标记所定义的适当的手指数量/一般模式组合。此外，还提出了当无法从标记（及其相对于其他标记的位置）确定这些元素时，获取手指间变异性的策略。这些结果可以立即在操作环境中对每个案件的手指间变异性进行估计。
