用心电图指导脉搏波识别个人，提升安全性和准确率
ECG-guided individual identification via PPG
- 通过跨模态知识蒸馏，将心电信号中的辨识信息迁移到脉搏波信号
- 在已见和未见个体识别上分别提升2.8%和3.0%的准确率
- 无需额外计算开销，适合嵌入式生物识别系统
基于脉搏波（PPG）的个体识别旨在通过内在心血管活动实现人类识别，因其高安全性与抗伪造能力受到广泛关注。然而，该技术受限于信息密度低，表现不佳。为此，本文引入心电图（ECG）作为新模态以增强输入信息密度。提出一种新颖的跨模态知识蒸馏框架，将ECG模态中的判别性知识无额外推理开销地迁移至PPG模态。为确保高效知识传递，分别设计基于CLIP的语义对齐模块与跨知识评估模块。大量实验表明，该框架在已见与未见个体识别上的整体准确率分别优于基线模型2.8%和3.0%。
原文摘要 · Abstract (English)
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.
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