基于心电与光电容积脉搏波特征层融合的身份识别方法

An Identity Recognition Method Based on ElectroCardioGraph and PhotoPlethysmoGraph Feature Fusion

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。

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(ECGPPG)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.