Okado Jessica Baleiro, da Camara E Silva Erick Simões, Sily Priscila Dias
Institute of Criminalistics, Superintendence of the Technical-Scientific Police, Team Santos, São Paulo, Brazil.
Brazil Federal Police, São Paulo, Brazil.
Forensic Sci Res. 2024 Nov 5;9(4):owae067. doi: 10.1093/fsr/owae067. eCollection 2024 Dec.
This study evaluates mathematical tools (principal component analysis, dynamic time warping, and the Kolmogorov-Smirnov hypothesis test) to analyse global and local data from dynamic signatures to reduce subjectivity and increase the reproducibility of handwriting examination using a two-step approach. A dataset composed of 1 800 genuine signature samples, 870 simulated signatures, and 60 disguises (30 formally similar or "autosimulated" and 30 random but different from usual) provided by 30 volunteers was collected. The first step involved global data analysis using principal component analysis and a hypothesis test performed for 62 global characteristics, and associations of these characteristics were analysed through calculations of multivariate distance followed by a hypothesis test. The second step involved the analysis of local characteristics including vertical and horizontal positions, speed, pressure gradient, acceleration, and jerk point-to-point, by using dynamic time warping followed by a hypothesis test. Optimization of sensitivity and specificity metrics of the hypothesis test was explored by varying its stringency and observing accuracy rates for the simulated and genuine groups. A -value threshold of 1 × 10 was found to be optimal, making the test more restrictive and yielding accuracy rates of 96.7% for genuine global data and 88.9% for simulated data. The same cut-off value for local characteristics provided an average accuracy rate of 95.4% for genuine samples and 94.7% for simulated samples, demonstrating high accuracy for both simulated and genuine samples. However, the method did not offer reasonable accuracy rates for disguises, consistent with observations in traditional handwriting examination. Our approach provided satisfactory results for forensic examination use. The visualization of graphs and signatures and analysis of all identifying elements of handwriting by the examining expert are still essential. In future studies, we plan to perform blind tests to validate our approach and propose a rigorous methodology.
本研究评估数学工具（主成分分析、动态时间规整和柯尔莫哥洛夫-斯米尔诺夫假设检验），以使用两步法分析动态签名的全局和局部数据，从而减少主观性并提高笔迹鉴定的可重复性。收集了由30名志愿者提供的包含1800个真实签名样本、870个模拟签名和60个伪装签名（30个形式相似或“自动模拟”的签名以及30个随机但与平常不同的签名）的数据集。第一步涉及使用主成分分析进行全局数据分析，并对62个全局特征进行假设检验，通过计算多元距离分析这些特征的关联，随后进行假设检验。第二步涉及通过使用动态时间规整并随后进行假设检验，分析局部特征，包括垂直和水平位置、速度、压力梯度、加速度以及逐点的急动度。通过改变假设检验的严格程度并观察模拟组和真实组的准确率，探索了该假设检验的灵敏度和特异性指标的优化。发现1×10的A值阈值是最优的，这使得检验更具限制性，真实全局数据的准确率为96.7%，模拟数据的准确率为88.9%。局部特征的相同截止值为真实样本提供了95.4%的平均准确率，为模拟样本提供了94.7%的平均准确率，表明模拟样本和真实样本的准确率都很高。然而，该方法对伪装签名的准确率不理想，这与传统笔迹鉴定中的观察结果一致。我们的方法为法医鉴定提供了令人满意的结果。由鉴定专家对手写笔迹的所有识别元素进行图形和签名的可视化以及分析仍然至关重要。在未来的研究中，我们计划进行盲测以验证我们的方法并提出一种严谨的方法。
