基于偏微分方程和基于样本的指纹重建方法

M. Rahmes, J. Allen, A. Elharti, G. Tenali
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引用次数: 13

摘要

手动潜在指纹重建以恢复缺失的脊是一个繁琐、耗时和昂贵的过程。潜在的指纹脊通常是部分污迹,部分缺失,老化等。这种类型的指纹不能直接用于法庭定罪,除非它能与已知的指纹相匹配。然而,潜在的指纹减少了寻找潜在嫌疑人和寻找失踪人口的工作。我们提出了一种自动化重建方法,最大限度地减少人工恢复。我们的非线性偏微分方程(PDE)和典型的喷漆过程可以帮助指纹专家。更大的缺失区域使用我们的基于相干的样本绘制算法进行修复。PDE涂漆用于填充脊状结构的小裂缝。脊线用各向异性扩散滤波器锐化。这些技术通过允许更多的细节来改进潜在指纹计算机匹配。描述了补漆缺失脊线的精度评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fingerprint Reconstruction Method Using Partial Differential Equation and Exemplar-Based Inpainting Methods
Manual latent fingerprint reconstruction to restore missing ridges is a tedious, time consuming, and expensive process. Latent fingerprint ridges are typically partially smudged, partially missing, aged, etc. This type of fingerprint cannot be used in the court of law directly to garner a conviction unless it can be matched to a known fingerprint. However, latent prints minimize the search for potential suspects and finding missing people. We propose an automated reconstruction method which minimizes manual restoration. Our nonlinear partial differential equation (PDE) and exemplar inpainting processes can aid the fingerprint expert. Larger missing regions are repaired using our coherent-based exemplar inpainting algorithm. PDE inpainting is used to fill small fissures in ridge structure. Ridge-lines are sharpened with anisotropic diffusion filters. These technologies improve latent fingerprint computer matching by allowing more minutiae. Accuracy assessment for inpainting missing ridges is described.
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