用可控数据增强生成高质量嫌疑人照片，提升识别准确率。
TeLL Me what you cant see
- 基于视觉-语言模型定制化生成嫌疑人图像。
- 在多种场景下显著提升识别准确率与鲁棒性。
- 适合刑侦部门用于补充稀缺或过时的面部图像。
在刑事调查中，嫌疑人的图像直接影响识别成功率。然而，执法机构常面临高质量图像稀缺或过时的问题，影响人员查找的准确性。本文提出一种新型法医制图增强框架，通过可定制的数据增强技术生成额外高质量图像，同时保持原始数据的生物特征完整性和一致性。实验表明，该方法在多种法医场景下显著提升了识别准确率与鲁棒性，验证了其作为执法应用可信工具的有效性。
原文摘要 · Abstract (English)
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.
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