Latent fingerprints segmentation based on Rearranged Fourier Subbands

2015 International Conference on Biometrics (ICB) Pub Date : 2015-05-19 DOI:10.1109/ICB.2015.7139063

Phumpat Ruangsakul, V. Areekul, Krisada Phromsuthirak, Arucha Rungchokanun

引用次数: 8

Abstract

In this work, we present a latent fingerprint segmentation algorithm based on spatial-frequency domain analysis. The algorithm arranges the overlapped block-based Fourier coefficients into groups of frequency and orientation subbands, called Rearranged Fourier Subband (RFS). The RFS reveals latent fingerprint spectra in only a limited number of subbands. The algorithm then boosts, sorts, and extracts, from complex background and noise, the latent fingerprint spectra in the RFS subbands. Several experiments are evaluated based on ground truth comparison, feature extraction, and latent matching on the NIST SD27 latent database. Our experimental results show that the proposed algorithm achieves better accuracy compared to those of the published automatic segmentation algorithms.

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基于重排傅立叶子带的潜在指纹分割

在这项工作中，我们提出了一种基于空频域分析的潜在指纹分割算法。该算法将重叠的基于块的傅里叶系数排列成频率和方向子带组，称为重排傅里叶子带(RFS)。RFS仅在有限的子带中显示潜在指纹光谱。然后，该算法从复杂的背景和噪声中增强、分类和提取RFS子带中的潜在指纹光谱。基于地面真值比较、特征提取和NIST SD27潜在数据库上的潜在匹配，对几个实验进行了评估。实验结果表明，与已有的自动分割算法相比，该算法具有更好的分割精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 International Conference on Biometrics (ICB)

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