Progressive Learning of a Diffusion-based Inpainting Model for Separating Overlapped Fingerprints

• URL: http://arxiv.org/abs/2608.03937v1
• Date: Tue, 04 Aug 2026 17:08:46 GMT
• Title: Progressive Learning of a Diffusion-based Inpainting Model for Separating Overlapped Fingerprints
• Abstract summary: Overlapped friction ridge patterns are a recurring problem in latent fingerprints recovered from crime scenes.<n>This work introduces a diffusion-based pipeline for separating component fingerprints from an image containing overlapping friction ridge patterns.
• Score: 35.645082991821155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Overlapped friction ridge patterns are a recurring problem in latent fingerprints recovered from crime scenes and in live-scan scenarios where residual fingerprints on the sensor may corrupt subsequent acquisitions. Existing approaches for separating overlapped fingerprints either rely on rule-based orientation field completion that requires strong domain knowledge or train end-to-end deep neural networks that do not account for domain-specific considerations. This work introduces a diffusion-based pipeline for separating component fingerprints from an image containing overlapping friction ridge patterns. We formulate the separation problem as an inpainting task and progressively learn a diffusion model for this task in multiple stages. Starting from a pre-trained Stable Diffusion model, we progressively incorporate a fingerprint prior, add the ability to complete partial fingerprints, and finally propose \textbf{overlap-aware inpainting} that reconstructs each component print using a diffusion inpainting model based on multi-channel conditioning. Experiments on two public datasets demonstrate that component fingerprints reconstructed using the proposed diffusion-based inpainting method can match with their mated counterparts with very high probability.

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