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

• URL: http://arxiv.org/abs/2511.04037v1
• Date: Thu, 06 Nov 2025 04:16:13 GMT
• Title: A Hybrid Deep Learning Model for Robust Biometric Authentication from Low-Frame-Rate PPG Signals
• Authors: Arfina Rahman, Mahesh Banavar,
• Abstract summary: Photoplethymography (volution) signals, which measure changes in blood volume in the skin using light, have recently gained attention in biometric authentication.<n>Photoplethymography signal quality is challenged by motion artifacts, illumination changes, and inter-subject physiological variability.<n>This study proposes a lightweight and cost-effective biometric authentication framework based on PPG signals extracted from low-frame-rate fingertip videos.
• Score: 0.34376560669160394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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