FlowID: 利用潜在流匹配模型增强法医鉴定
FlowID : Enhancing Forensic Identification with Latent Flow-Matching Models
AI总结：
提出FlowID方法，结合单图像微调与注意力掩码，修复暴力死亡面部损伤并保留身份特征，在InjuredFaces基准上优于现有开源方法。
摘要：
每天，许多人因犯罪、战争、移民或气候灾难在暴力环境中死亡。法医和法律执法机构记录了许多死者的肖像作为证据，但无法立即进行身份鉴定。虽然传统图像编辑工具可以处理这些照片以供公开发布，但工作流程冗长且效果欠佳。在这项工作中，我们利用图像生成模型的进展（现在可以生成逼真的人像）引入了FlowID，一种保持身份的面部重建方法。我们的方法结合了单图像微调（使生成模型适应分布外的受伤面部）和基于注意力的掩码（将编辑定位到受损区域，同时保留身份关键特征）。这些组件共同作用，能够去除暴力死亡的伪影，同时保留足够的身份信息以支持鉴定。为了评估我们的方法，我们引入了InjuredFaces，一个用于严重面部损伤下保持身份的面部重建的新基准。除了作为本工作的评估工具外，InjuredFaces还为社区提供了一个标准化资源，用于研究和比较处理极端条件下面部重建的方法。实验结果表明，FlowID优于最先进的开源方法，同时保持低内存需求，使其适合本地部署而不损害数据隐私。
英文摘要：
Every day, many people die under violent circumstances, whether from crimes, war, migration, or climate disasters. Medico-legal and law enforcement institutions document many portraits of the deceased for evidence, but cannot immediately carry out identification on them. While traditional image editing tools can process these photos for public release, the workflow is lengthy and produces suboptimal results. In this work, we leverage advances in image generation models, which can now produce photorealistic human portraits, to introduce FlowID, an identity-preserving facial reconstruction method. Our approach combines single-image fine-tuning, which adapts the generative model to out-of-distribution injured faces, with attention-based masking that localizes edits to damaged regions while preserving identity-critical features. Together, these components enable the removal of artifacts from violent death while retaining sufficient identity information to support identification. To evaluate our method, we introduce InjuredFaces, a novel benchmark for identity-preserving facial reconstruction under severe facial damage. Beyond serving as an evaluation tool for this work, InjuredFaces provides a standardized resource for the community to study and compare methods addressing facial reconstruction in extreme conditions. Experimental results show that FlowID outperforms state-of-the-art open-source methods while maintaining low memory requirements, making it suitable for local deployment without compromising data privacy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
