指纹(潜在指纹)质量的评估是法医调查的固有部分。指纹质量表明在法医调查过程中从犯罪现场回收的痕迹证据的价值和效用;它确定如何处理证据,并且它与在参考数据集中找到相应指纹的概率相关。指纹在随机表面上的沉积以不受控制的方式自发发生,这给摩擦脊图案的结果带来了缺陷。在这项工作中,我们提出了一种新的自动指纹质量评估(AFQA)概率框架。我们使用了现代深度学习技术,它们甚至能够从嘈杂的数据中提取模式,并将它们与可解释AI(XAI)领域的方法相结合,使我们的模型更加透明。我们的解决方案首先预测质量概率分布,然后,我们从中计算最终质量值,如果需要,模型的不确定性。此外,我们用相应的质量图补充了预测的质量值。我们使用GradCAM来确定指纹的哪些区域对整体质量预测的影响最大。我们表明,生成的质量图与输入图像中细节点的密度高度相关。我们的深度学习方法实现了较高的回归性能,同时显著提高预测的可解释性和透明度。
The assessment of
fingermark (latent fingerprint) quality is an intrinsic part of a forensic investigation. The
fingermark quality indicates the value and utility of the trace evidence recovered from the crime scene in the course of a forensic investigation; it determines how the evidence will be processed, and it correlates with the probability of finding a corresponding fingerprint in the reference dataset. The deposition of fingermarks on random surfaces occurs spontaneously in an uncontrolled fashion, which introduces imperfections to the resulting impression of the friction ridge pattern. In this work, we propose a new probabilistic framework for Automated
Fingermark Quality Assessment (AFQA). We used modern deep learning techniques, which have the ability to extract patterns even from noisy data, and combined them with a methodology from the field of eXplainable AI (XAI) to make our models more transparent. Our solution first predicts a quality probability distribution, from which we then calculate the final quality value and, if needed, the uncertainty of the model. Additionally, we complemented the predicted quality value with a corresponding quality map. We used GradCAM to determine which regions of the
fingermark had the largest effect on the overall quality prediction. We show that the resulting quality maps are highly correlated with the density of minutiae points in the input image. Our deep learning approach achieved high regression performance, while significantly improving the interpretability and transparency of the predictions.