标题： 一种用于从低帧率PPG信号中进行鲁棒生物特征认证的混合深度学习模型 显示英文标题

标题： A Hybrid Deep Learning Model for Robust Biometric Authentication from Low-Frame-Rate PPG Signals

摘要： 光电容积描记法（PPG）信号通过使用光测量皮肤中血容量的变化，最近在生物识别认证中受到关注，因为其具有非侵入性获取、固有的活体检测和适合低成本可穿戴设备的特点。 然而，PPG信号质量受到运动伪影、照明变化和个体间生理变异的挑战，使得鲁棒的特征提取和分类至关重要。 本研究提出了一种基于从低帧率指尖视频中提取的PPG信号的轻量级且成本效益高的生物识别认证框架。 采用CFIHSR数据集进行评估，该数据集包含46名受试者在14 Hz采样率下的PPG记录。 原始PPG信号经过标准预处理流程，包括基线漂移去除、使用主成分分析（PCA）抑制运动伪影、带通滤波、基于傅里叶的重采样和振幅归一化。 为了生成鲁棒表示，每个一维PPG段通过连续小波变换（CWT）转换为二维时频标量图，有效捕捉瞬时心血管动态。 我们开发了一个混合深度学习模型，称为CVT-ConvMixer-LSTM，通过将卷积视觉变压器（CVT）和ConvMixer分支的空间特征与长短期记忆网络（LSTM）的时间特征相结合。 在46名受试者的实验结果表明认证准确率为98%，验证了模型对噪声和受试者之间差异的鲁棒性。 由于其高效性、可扩展性和固有的活体检测能力，所提出的系统非常适合现实世界中的移动和嵌入式生物识别安全应用。 显示英文摘要

摘要： Photoplethysmography (PPG) signals, which measure changes in blood volume in the skin using light, have recently gained attention in biometric authentication because of their non-invasive acquisition, inherent liveness detection, and suitability for low-cost wearable devices. However, PPG signal quality is challenged by motion artifacts, illumination changes, and inter-subject physiological variability, making robust feature extraction and classification crucial. This study proposes a lightweight and cost-effective biometric authentication framework based on PPG signals extracted from low-frame-rate fingertip videos. The CFIHSR dataset, comprising PPG recordings from 46 subjects at a sampling rate of 14 Hz, is employed for evaluation. The raw PPG signals undergo a standard preprocessing pipeline involving baseline drift removal, motion artifact suppression using Principal Component Analysis (PCA), bandpass filtering, Fourier-based resampling, and amplitude normalization. To generate robust representations, each one-dimensional PPG segment is converted into a two-dimensional time-frequency scalogram via the Continuous Wavelet Transform (CWT), effectively capturing transient cardiovascular dynamics. We developed a hybrid deep learning model, termed CVT-ConvMixer-LSTM, by combining spatial features from the Convolutional Vision Transformer (CVT) and ConvMixer branches with temporal features from a Long Short-Term Memory network (LSTM). The experimental results on 46 subjects demonstrate an authentication accuracy of 98%, validating the robustness of the model to noise and variability between subjects. Due to its efficiency, scalability, and inherent liveness detection capability, the proposed system is well-suited for real-world mobile and embedded biometric security applications.