AI 中文总结
本研究针对重叠指纹分离问题，提出渐进式学习的基于扩散的修复模型，结合指纹先验与多通道条件的感知重叠修复，在公开数据集上实现了高匹配度的组成指纹重建。
AI 中文摘要
重叠的摩擦脊纹是犯罪现场提取的潜在指纹以及传感器上残留指纹可能破坏后续采集的活扫描场景中反复出现的问题。现有分离重叠指纹的方法要么依赖需要强领域知识的基于规则的方向场补全，要么训练不考虑领域特定考量的端到端深度神经网络。本研究提出一种基于扩散的流程，用于从包含重叠摩擦脊纹的图像中分离出各组成指纹。我们将分离问题建模为一项修复任务，并分多个阶段渐进式学习该任务的扩散模型。从预训练的Stable Diffusion模型出发，我们逐步融入指纹先验，增加补全部分指纹的能力，最终提出基于多通道条件的扩散修复模型，用于重建各组成指纹的感知重叠修复。在两个公开数据集上的实验表明，所提出的基于扩散的修复方法重建的组成指纹，能够以极高概率与其匹配的对应指纹相匹配。
英文摘要
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.
CommentsAccepted to IJCB 2026
