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

2025-11-06

AI Summary

Robust authentication using low-frame-rate fingertip video-based photoplethysmography (PPG) signals remains challenging due to motion artifacts, illumination variations, and inter-subject physiological variability. To address this, we propose a lightweight spatiotemporal fusion deep learning framework. Our approach introduces a novel hybrid model—CVT-ConvMixer-LSTM—that jointly captures time-frequency domain features and long-term temporal dependencies. Time-frequency features are extracted via principal component analysis (PCA)-based denoising, bandpass filtering, Fourier-domain resampling, amplitude normalization, and continuous wavelet transform (CWT). The integrated architecture significantly enhances noise robustness and cross-subject generalization. Evaluated on the 46-subject CFIHSR dataset, our method achieves 98% authentication accuracy. Results demonstrate its efficacy and practicality for resource-constrained mobile and embedded security applications.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Robust biometric authentication from low-frame-rate PPG signals
• Overcoming motion artifacts and physiological variability in PPG data
• Developing hybrid deep learning model for accurate identity verification

Innovation

• Hybrid deep learning model CVT-ConvMixer-LSTM
• Preprocessing with PCA and CWT time-frequency scalograms
• Robust authentication from low-frame-rate PPG signals