Abstract
During criminal investigations, images of persons of interest directly influence the success of identification procedures. However, law enforcement agencies often face challenges related to the scarcity of high-quality images or their obsolescence, which can affect the accuracy and success of people searching processes. This paper introduces a novel forensic mugshot augmentation framework aimed at addressing these limitations. Our approach enhances the identification probability of individuals by generating additional, high-quality images through customizable data augmentation techniques, while maintaining the biometric integrity and consistency of the original data. Several experimental results show that our method significantly improves identification accuracy and robustness across various forensic scenarios, demonstrating its effectiveness as a trustworthy tool law enforcement applications. Index Terms: Digital Forensics, Person re-identification, Feature extraction, Data augmentation, Visual-Language models.
Abstract (translated)
在刑事调查中，涉案人员的图像直接影响到识别程序的成功率。然而，执法机构常常面临高质量图片稀缺或过时的问题，这些问题会影响搜寻和确认身份过程的准确性和成功率。本文介绍了一种新的法医照片增强框架，旨在解决这些限制。我们的方法通过使用可定制的数据增强技术生成额外的、高质量的图像来提高个体识别的概率，同时保持原始数据的生物测量完整性和一致性。多个实验结果表明，我们提出的方法在各种法医场景中显著提高了识别准确率和鲁棒性，展示了其作为执法应用中的可信工具的有效性。 关键词：数字取证，人员再识别，特征提取，数据增强，视觉-语言模型。
URL
https://arxiv.org/abs/2503.19478
