李思卓,
董威,
李清华,
胡芳
长安大学电子与控制工程学院 西安 710064
基金项目:陕西省重点研发计划项目(2021GY-054),西安市科技创新引导项目(20180504YD23CG29(1))
详细信息
作者简介:肖剑:男,1975年生,副教授,研究方向为模式识别与智能系统
李思卓:女,1997年生,硕士生,研究方向为新型生物特征识别技术
董威:男,1994年生,硕士,研究方向为新型生物特征识别技术
李清华:女,1975年生,博士,研究方向为模式识别与智能系统
胡芳:女,1995年生,硕士生,研究方向为智能可穿戴设备及系统
通讯作者:肖剑 xiaojian@chd.edu.cn
中图分类号:TP911.7计量
文章访问数:380
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被引次数:0
出版历程
收稿日期:2020-10-22
修回日期:2021-03-16
网络出版日期:2021-04-12
刊出日期:2021-10-18
An Identity Recognition Method Based on ElectroCardioGraph and PhotoPlethysmoGraph Feature Fusion
Jian XIAO,,Sizhuo LI,
Wei DONG,
Qinghua LI,
Fang HU
School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China
Funds:The Key Project of Research and Development Program of Shaanxi Province of China (2021GY-54),Xi’an Science and Technology Innovation Guiding Project (20180504YD23CG29(1))
摘要
摘要:针对单模态的心电信号(ECG)或光电容积脉搏波信号(PPG)识别技术中存在的精度不高,未考虑类内相关性等问题,该文提出基于判别相关分析法(DCA)对ECG与PPG组合特征矩阵进行特征层融合以及对K-最近邻(KNN)和支持向量机(SVM)分类器在决策层融合的识别方法。实验结果表明,使用融合特征(ECG-PPG)与融合分类器(KNN-SVM)的方法对23名受试者进行分类识别的准确率可以达到98.2%,识别精度在常规环境下优于单模态识别。为多模生物特征身份识别提供了一种有效模型。
关键词:心电信号/
光电容积脉搏波信号/
多生物特征识别/
特征融合/
判别相关分析
Abstract:Because single mode ElectroCardioGraph (ECG) and PhotoPlethysmoGraph(PPG) existed problem with the low recognition accuracy, not considering intra-class correlation, this paper proposes a recognition method based on the Discriminant Correlation Analysis (DCA) for the feature layer fusion of the ECG and PPG combined feature matrix and the fusion of the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers at the decision layer. The experimental results show that the use of fusion features (ECG-PPG) and fusion the classifier (KNN-SVM) method can classify and recognize 23 subjects with an accuracy of 98.2%, and the recognition accuracy is better than single-modal recognition in the conventional environment. It provides an effective model for multimodal biometric identification.
Key words:ElectroCardioGraph (ECG)/
PhotoPlethysmoGraph (PPG)/
Multimodal biometric recognition/
Feature fusion/
Discriminant Correlation Analysis (DCA)
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