用低帧率手指视频做生物识别，准确率达98%。
A Hybrid Deep Learning Model for Robust Biometric Authentication from Low-Frame-Rate PPG Signals
- 将一维PPG信号转为二维时频图，结合CVT、ConvMixer和LSTM提取特征
- 在46人数据集上实现98%认证准确率，抗运动伪影和个体差异
- 轻量高效，适合可穿戴设备，自带活体检测能力
光电体积描记（PPG）信号通过光测量皮肤血容量变化，因其非侵入式采集、天然活体检测能力及适用于低成本可穿戴设备而受到关注。然而，运动伪影、光照变化和个体生理差异导致信号质量下降，需鲁棒的特征提取与分类方法。本研究提出一种基于低帧率指尖视频提取的轻量级、低成本生物识别框架。采用包含46名受试者、采样率为14 Hz的CFIHSR数据集进行评估。原始PPG信号经基线漂移去除、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及幅度归一化等预处理。为生成鲁棒表征，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。提出混合深度学习模型CVT-ConvMixer-LSTM，融合卷积视觉变压器（CVT）与ConvMixer的空间特征及长短期记忆网络（LSTM）的时序特征。实验在46名受试者上验证了98%的认证准确率，证明模型对噪声和个体差异具有鲁棒性。由于其高效性、可扩展性及内在活体检测能力，该系统适用于真实世界移动与嵌入式生物安全应用。
原文摘要 · Abstract (English)
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.
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。