Multi-view discriminant analysis with sample diversity for ECG biometric recognition
Multi-view discriminant analysis with sample diversity for ECG biometric recognition
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DOI：
10.1016/j.patrec.2021.01.027
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发表时间：
2021-02-19
影响因子：
5.1
通讯作者：
Yin, Yilong
中科院分区：
文献类型：
--
作者：
Huang, Yuwen;Yang, Gongping;Yin, Yilong
心电图生物特征识别是当前的一个新的研究方向，已经发展了许多方法。由于生理活动和心理活动的影响，同一个人的心跳会出现不同的情况。然而，现有的ECG生物特征识别方法没有利用样本的多样性信息。在本文中，我们提出了一个多视角的判别分析方法，考虑到样本的多样性，心电生物特征识别。首先，我们提出了一种利用单导联心电信号生成多视图的方法。其次，我们提出了一个多视图学习框架，它考虑到样本的多样性，以产生一个更具鉴别力的子空间。第三，为了获得更鲁棒的解决方案，我们引入了一个去噪约束来学习不同视图之间的关系，这可以创建一个稳定的表示来对抗ECG噪声。最后，在四个数据库上的实验结果表明，与现有的ECG生物特征识别方法相比，该方法具有较好的识别性能。nbsp;（c）2021 Elsevier B. V.保留所有权利。
Currently, electrocardiogram (ECG) biometric recognition is a novel research trend, and many methods have been developed. Due to the influence of physical and psychological activities, there are heartbeats diversities of the same person. However, the existing ECG biometric recognition methods do not make use of sample diversity information. In this paper, we present a multi-view discriminant analysis approach in the consideration of sample diversity for ECG biometric recognition. Firstly, we propose a method of generating multiple views by using single lead ECG signal. Secondly, we present a multi-views learning framework, which takes sample diversity into account to generate a more discriminative subspace. Thirdly, to obtain a more robust solution, we introduce a denoising constraint to learn the relationships between different views, which can create a stable representation against ECG noise. At last, experimental results demonstrate that compared with the state-of-the-art methods on four databases, the proposed method can achieve competitive performance compared to state-of-the-art ECG biometric recognition methods.& nbsp; (c) 2021 Elsevier B.V. All rights reserved.