利用心跳周期性特征，从人脸视频中分离出微弱的脉搏信号。
Time-varying rPPG signal separation via block-sparse signal model
- 将脉搏信号的准周期性建模为时频域的块稀疏结构
- 在光照变化下仍能自适应分离出稳定脉搏信号
- 适合需要非接触式心率监测的应用场景
远程光电容积脉搏波描记（rPPG）通过分析面部视频中的细微颜色变化，实现非接触式心脏脉搏信号测量。然而，由于信号极弱且易受光照噪声干扰，提取rPPG信号仍具挑战。本文提出一种rPPG信号提取方法，利用脉搏信号的准周期特性——源于稳定的的心脏节律——将其在时频域建模为块稀疏结构。为融合块稀疏模型并实现在光照波动下的自适应信号分离，构建了时变信号分离框架。在公开数据集上的实验验证了该方法的有效性。
原文摘要 · Abstract (English)
Remote photoplethysmography (rPPG) enables non-contact measurement of cardiac pulse signals by analyzing subtle color changes in facial videos. Nevertheless, extracting rPPG signals remains challenging because of their extremely weak signal strength and susceptibility to illumination noise. In this paper, we propose an rPPG signal extraction method that exploits the quasi-periodic characteristics of rPPG signals. Our approach models quasi-periodicity of the rPPG signal, which arises from the stable cardiac cycle, as a block-sparse structure in the time-frequency domain. To incorporate a block-sparse model and enable adaptive signal separation under illumination fluctuations, we construct a time-varying signal separation framework. Experiments using a public dataset demonstrate the effectiveness of our method.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。