Fingermark imaging and quantification: optical enhancement, surface and modelling analysis

Fingermarks are regarded as reliable evidence in criminal investigation and individual identity; however, their changes with pressure and substrates, which can provide clues for case investigation, remain unknown. To advance fingermark quantification, we propose a systematic mathematical model framework to analyse the quantitative data collected by a non-destructive fingerprint imaging and surface analysis technique, which ensures the primitiveness and integrity of the fingermark and exhibits excellent performance in fingermark imaging, even including latent fingermarks that were aged for 60 days. Based on statistical and modelling analysis, for the first time we discover a logarithmic correlation between friction ridge and pressure. Moreover, smoother substrates generally result in wider ridges and exert more significant impacts on fingermarks, such as transparent glass emerging as the smoothest substrate, with the widest ridges and the highest influence weight at 21.2%, which has been confirmed by surface analysis. Notably, the interaction effect between pressure and substrates on fingermarks is first corroborated. Extensive experiments demonstrate the competitive results based on the mathematical models, and the proposed quantitative relationship among friction ridge, pressure and substrate provides a new perspective for investigating the connection between fingermarks and their influencing factors.

指纹痕迹（Fingermarks）在刑事侦查与个体身份鉴定领域被视作可靠的证据；然而，其随按压力度与承痕载体的变化规律——本可为案件侦查提供关键线索——迄今仍未被阐明。为推动指纹痕迹的量化研究，本文提出一套系统化的数学模型框架，用于分析由无损指纹成像与表面分析技术采集的量化数据。该技术可完整保留指纹痕迹的原始性与完整性，且在指纹成像方面表现出色，甚至可对留存60天的潜在指纹痕迹（latent fingermarks）实现有效成像。基于统计与建模分析，本研究首次揭示了摩擦嵴（friction ridge）与按压力度之间的对数相关性。此外，承痕载体表面越光滑，通常会使指纹嵴线越宽，且对指纹痕迹的影响效应越显著：例如透明玻璃作为最光滑的承痕载体，其对应的指纹嵴线最宽，影响权重高达21.2%，该结论已通过表面分析得到验证。值得关注的是，本研究首次证实了按压力度与承痕载体二者对指纹痕迹的交互作用效应。大量实验结果表明，所提出的数学模型具备优异的竞争性能；而本文所建立的摩擦嵴、按压力度与承痕载体间的量化关系，为探究指纹痕迹与其影响因素间的内在关联提供了全新视角。