ECG-guided individual identification via PPG

TL;DR

This paper introduces a cross-modal knowledge distillation framework that enhances PPG-based individual identification by leveraging ECG signals, significantly improving recognition accuracy without extra inference costs.

Contribution

It proposes a novel framework combining ECG and PPG modalities with CLIP-based alignment for improved biometric recognition accuracy.

Findings

Outperforms baseline with 2.8% and 3.0% accuracy improvements on seen and unseen individuals.

Utilizes cross-modal knowledge transfer to enhance PPG recognition.

Achieves high security and resistance to mimicry in biometric identification.

Abstract

Photoplethsmography (PPG)-based individual identification aiming at recognizing humans via intrinsic cardiovascular activities has raised extensive attention due to its high security and resistance to mimicry. However, this kind of technology witnesses unpromising results due to the limitation of low information density. To this end, electrocardiogram (ECG) signals have been introduced as a novel modality to enhance the density of input information. Specifically, a novel cross-modal knowledge distillation framework is implemented to propagate discriminate knowledge from ECG modality to PPG modality without incurring additional computational demands at the inference phase. Furthermore, to ensure efficient knowledge propagation, Contrastive Language-Image Pre-training (CLIP)-based knowledge alignment and cross-knowledge assessment modules are proposed respectively. Comprehensive experiments are conducted and results show our framework outperforms the baseline model with the improvement of 2.8% and 3.0% in terms of overall accuracy on seen- and unseen individual recognitions.