利用 SVD 和不变矩的修正 PCA 增强指纹分类。

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Frontiers in Artificial Intelligence Pub Date : 2024-08-05 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1433494
Ala Balti, Abdelaziz Hamdi, Sabeur Abid, Mohamed Moncef Ben Khelifa, Mounir Sayadi
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引用次数: 0

摘要

本研究介绍了一种用于指纹识别的新型 MOMENTS-SVD 向量,它结合了不变矩和 SVD(奇异值分解),并通过改进的 PCA(主成分分析)进行了增强。我们的方法利用 SVD 和不变矩提取独特的指纹特征,然后利用欧氏距离和神经网络进行分类。MOMENTS-SVD 向量降低了计算复杂度,优于现有模型。通过对不同数据库(CASIA V5、FVC 2002、2004、2006)使用等效误差率 (EER) 和 ROC 曲线进行比较研究,评估了我们的方法与 ResNet、VGG19、神经模糊、DCT 特征和不变矩的比较,证明我们的方法具有更高的准确性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced fingerprint classification through modified PCA with SVD and invariant moments.

This research introduces a novel MOMENTS-SVD vector for fingerprint identification, combining invariant moments and SVD (Singular Value Decomposition), enhanced by a modified PCA (Principal Component Analysis). Our method extracts unique fingerprint features using SVD and invariant moments, followed by classification with Euclidean distance and neural networks. The MOMENTS-SVD vector reduces computational complexity by outperforming current models. Using the Equal Error Rate (EER) and ROC curve, a comparative study across databases (CASIA V5, FVC 2002, 2004, 2006) assesses our method against ResNet, VGG19, Neuro Fuzzy, DCT Features, and Invariant Moments, proving enhanced accuracy and robustness.

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来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
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