Title: Real-time biometric recognition using photoplethysmography signals and deep learning approaches

Authors: Ali Cherry; Hayat Kourani; Wassim Salameh; Mohamad Hajj-Hassan; Mohamad Abou Ali; Soumaya Berro

Addresses: Department of Biomedical Engineering, Lebanese International University, Beirut, Lebanon; Department of Biomedical Engineering, International University of Beirut, Beirut, Lebanon ' Department of Biomedical Engineering, Lebanese International University, Beirut, Lebanon ' Department of Mechanical Engineering, Lebanese International University, Beirut, Lebanon; Department of Mechanical Engineering, International University of Beirut, Beirut, Lebanon ' Department of Biomedical Engineering, Lebanese International University, Beirut, Lebanon ' Department of Biomedical Engineering, Lebanese International University, Beirut, Lebanon ' Department of Biomedical Engineering, Lebanese International University, Beirut, Lebanon

Abstract: This research investigates photoplethysmography (PPG) signals as a reliable, non-invasive method for biometric recognition, addressing modern security needs with a unique and hard-to-replicate approach. A dedicated acquisition system was designed to collect high-quality PPG data from 40 individuals under a strict protocol, ensuring data consistency and accuracy. Advanced filtration minimised noise, while preprocessing steps - including data augmentation and normalisation - enhanced dataset diversity, optimising model training. Five deep learning models were evaluated: a 1D convolutional neural network (1D CNN), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), gated recurrent unit (GRU) and dense neural network (DNN). The 1D CNN performed exceptionally well, achieving a classification accuracy of 99.94%, making it ideal for real-time identification applications. These findings underscore the promise of PPG signals for biometric systems and demonstrate the powerful advantages of deep learning, particularly the 1D CNN, in delivering high identification accuracy across varied environments and practical applications.

Keywords: biometric identification; deep learning; photoplethysmography; signals processing; real-time processing; classification accuracy; graphical user interface.

DOI: 10.1504/IJBM.2026.154564

International Journal of Biometrics, 2026 Vol.18 No.4, pp.387 - 414

Received: 27 Dec 2024
Accepted: 20 Jun 2025
Published online: 06 Jul 2026 *

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