FlowID: Enhancing Forensic Identification with Latent Flow-Matching Models

Abstract

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.