Amsterdam University of Applied Sciences, Weesperzijde 190, 1097 DZ Amsterdam, The Netherlands; VU University Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands; Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB The Hague, The Netherlands.
Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB The Hague, The Netherlands.
Forensic Sci Int. 2019 Sep;302:109904. doi: 10.1016/j.forsciint.2019.109904. Epub 2019 Jul 30.
Fingermarks are highly relevant in criminal investigations for individualization purposes. In some cases, the question in court changes from 'Who is the source of the fingermarks?' to 'How did the fingermark end up on the surface?'. In this paper, we explore the evaluation of fingermarks given activity level propositions by using Bayesian networks. The variables that provide information on activity level questions for fingermarks are identified and their current state of knowledge with regards to fingermarks is discussed. We identified the variables transfer, persistency, recovery, background fingermarks, location of the fingermarks, direction of the fingermarks, the area of friction ridge skin that left the mark and pressure distortions as variables that may provide information on how a fingermark ended up on a surface. Using three case examples, we show how Bayesian networks can be used for the evaluation of fingermarks given activity level propositions.
指纹在犯罪调查中对于个体识别非常重要。在某些情况下，法庭上的问题从“指纹的来源是谁？”转变为“指纹是如何出现在表面上的？”。在本文中，我们探讨了使用贝叶斯网络评估给定活动水平假设的指纹。确定了提供指纹活动水平问题信息的变量，并讨论了它们在指纹方面的现有知识状态。我们确定了变量转移、持久性、恢复、背景指纹、指纹位置、指纹方向、留下痕迹的摩擦脊皮肤区域以及压力变形，这些变量可能提供有关指纹如何出现在表面上的信息。通过三个案例示例，我们展示了如何使用贝叶斯网络来评估给定活动水平假设的指纹。
