论文类型：

期刊论文

论文编号：

2503915

发表刊物：

IEEE Transactions on Instrumentation and Measurement

收录刊物：

SCI

卷号：

74

关键字：

Ballistocardiogram (BCG) signals fingerprinting nonorthogonal space projection person identification progressive metric learning

DOI码：

10.1109/TIM.2025.3527591

发表时间：

2025-01-09

影响因子：

5.6

摘要：

Repeated personal identity verification is a tedious but crucial everyday task for people to access sensitive information and private assets. Being able to protect individuals’ rights while alleviating the burden of repetitive verification, unobtrusive person identification methods are attracting increasing attention. Since it contains information reflecting a person’s cardiovascular activities, the ballistocardiogram (BCG) signal has emerged as a noninvasive measure for biometric recognition. However, complex components and diverse waveforms of BCG signals pose challenges in effectively extracting identity information. To tackle these problems, we present MuSFId, a novel multistage fingerprinting-based identification approach for BCG signals. First, we introduce a nonorthogonal space projection algorithm to precisely extract heartbeat components from composite signals, generating a refined heartbeat waveform. Subsequently, we implement progressive metric learning to project these heartbeat segments into latent spaces, facilitating the separation of features unique to each individual. Furthermore, we devise a fingerprinting strategy to create identifiers for accurate individual matching. To validate the effectiveness of MuSFId, we conducted experiments using the Kansas dataset and private data collected with a low-cost PVDF sensor. The results demonstrate that MuSFId significantly outperforms existing methods, achieving a recognition accuracy of over 99%, underling its promising application potential.