An Identity Recognition Method Based on ElectroCardioGraph and PhotoPlethysmoGraph Feature Fusion

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.