Enhanced fingerprint classification through modified PCA with SVD and invariant moments.

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

Abstract

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.

利用 SVD 和不变矩的修正 PCA 增强指纹分类。
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。
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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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