ECG biometric authentication based on non-fiducial approach using kernel methods
ECG biometric authentication based on non-fiducial approach using kernel methods
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DOI：
10.1016/j.dsp.2016.02.008
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发表时间：
2016-05-01
影响因子：
2.9
通讯作者：
Aziz, Ahmad Fazli Abdul
中科院分区：
文献类型：
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作者：
Hejazi, Maryamsadat;Al-Haddad, S. A. R.;Aziz, Ahmad Fazli Abdul
身份识别面临着几个挑战，特别是在提取个人的独特功能，从生物特征模态和模式分类。例如，心电图（ECG）波形具有人类识别的独特身份属性，并且它们的信号不是周期性的。目前，为了生成一个有意义的ECG特征集，使用基于自相关（AC）结合线性降维方法的非基准方法。本文提出了一种新的非基准的ECG生物特征验证框架，使用核方法降低高自相关向量的维数和识别系统后，52个主题的离散小波变换（DWT）的信号去噪。为了评估多类支持向量机（SVM）与一对一（OAA）方法的验证性能，研究了不同降维技术对特征提取的影响.实验结果表明，与线性判别分析（LDA）和主成分分析（PCA）相比，核主成分分析（KPCA）的高斯OAA支持向量机在随机未知ECG数据集上具有更高的测试识别率。(C)2016 Elsevier Inc. All rights reserved.
Identity recognition faces several challenges especially in extracting an individual's unique features from biometric modalities and pattern classifications. Electrocardiogram (ECG) waveforms, for instance, have unique identity properties for human recognition, and their signals are not periodic. At present, in order to generate a significant ECG feature set, non-fiducial methodologies based on an autocorrelation (AC) in conjunction with linear dimension reduction methods are used. This paper proposes a new non-fiducial framework for ECG biometric verification using kernel methods to reduce both high autocorrelation vectors' dimensionality and recognition system after denoising signals of 52 subjects with Discrete Wavelet Transform (DWT). The effects of different dimensionality reduction techniques for use in feature extraction were investigated to evaluate verification performance rates of a multi-class Support Vector Machine (SVM) with the One-Against-All (OAA) approach. The experimental results demonstrated higher test recognition rates of Gaussian OAA SVMs on random unknown ECG data sets with the use of the Kernel Principal Component Analysis (KPCA) as compared to the use of the Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). (C) 2016 Elsevier Inc. All rights reserved.