Heart signatures: Open-set person identification based on cardiac radar signals

• Published in: Biomedical signal processing and control Vol. 72; p. 103306
• Main Authors: Yan, Baiju, Zhang, Hao, Yao, Yicheng, Liu, Changyu, Jian, Pu, Wang, Peng, Du, Lidong, Chen, Xianxiang, Fang, Zhen, Wu, Yirong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2022
• Subjects: Cardiac radar heartbeat signatures; Dipole deep learning model (DDLM); Machine learning; Non-contact biometric identification; Open set identification; Non-contact biometric identification; Cardiac radar heartbeat signatures; Open set identification; Machine learning; Dipole deep learning model (DDLM)
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2021.103306

•We explore radar-based heartbeat identification under open-set condition initially.•We propose a novel DDLM model without heartbeat segmentation and feature engineering.•The method shows great effectiveness in both open-set and closed-set environment. Non-contact continuous biometric identification system based on the heartbeat signals has attracted more attention due to its privacy-friendly properties. Current methods, however, mostly focused on the traditional machine learning methods under the close-set condition. This paper aims to investigate the feasibility of using the cardiac radar heartbeat signals and deep learning techniques to identify person in the open-set environment. A novel dipole deep learning model (DDLM) was proposed for the open-set person identification problem without heartbeat segmentation and feature engineering. The normalized heartbeat samples with time duration of 5 s were used as input and encoded into the feature space, where the encoded features of the same person cluster closely around the corresponding negative pole and repel far from positive pole, and those of different persons separate loosely from each other. Finally, threshold on the distance from the features to the dipoles in the feature space was set for each known identity. Extensive experiments conducted on a public dataset of clinically recorded vital signs indicate that:(1) The proposed model shows high stability under close-set condition with an accuracy higher than 99 % with 30 subjects.(2) The accuracy and the F1-score attain 93.42 % and 93.57 % under the open-set condition with the maximum openness of 29.3 %, respectively. The proposed model shows high effectiveness in person identification using heartbeat signals. The DDLM outperforms most of the existing methods under the close-set condition. And the DDLM shows a promising future for person identification in open-set environment.