Radar-Based Identification of Individuals Using Heartbeat Features Extracted from Signal Amplitude and Phase
Haruto Kobayashi, Takuya Sakamoto
TL;DR
This work tackles non-contact identification using heartbeat features extracted from millimeter-wave radar. It systematically compares information captured by amplitude, phase, and complex-signal spectrograms and then fuses them via a concatenated feature vector $r_{\mathrm{prop}}$ fed to a support vector machine. The key finding is that the fused representation leverages complementary information across signal components, delivering high accuracy ($97.67\%$) and near-perfect AUC ($0.999$) for 60-s observations, with strong but lower performance for 5-s data ($91\%$). This approach demonstrates the potential for rapid, unobtrusive biometric identification in practical settings, while highlighting the need for larger studies to validate robustness across diverse populations and conditions.
Abstract
This study proposes a non-contact method for identifying individuals through the use of heartbeat features measured with millimeter-wave radar. Although complex-valued radar signal spectrograms are commonly used for this task, little attention has been paid to the choice of signal components, namely, whether to use amplitude, phase, or the complex signal itself. Although spectrograms can be constructed independently from amplitude or phase information, their respective contributions to identification accuracy remain unclear. To address this issue, we first evaluate identification performance using spectrograms derived separately from amplitude, phase, and complex signals. We then propose a feature fusion method that integrates these three representations to enhance identification accuracy. Experiments conducted with a 79-GHz radar system and involving six participants achieved an identification accuracy of 97.67%, demonstrating the effectiveness of the proposed component-wise analysis and integration approach.