Real-Time PPG-Based Biometric Identification: Advancing Security with 2D Gram Matrices and Deep Learning Models

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 1; p. 40
• Main Authors: Cherry, Ali, Nasser, Aya, Salameh, Wassim, Abou Ali, Mohamad, Hajj-Hassan, Mohamad
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2025; MDPI
• Subjects: Accuracy; Adult; Algorithms; Artificial intelligence; Biometric identification; Biometric Identification - methods; biometric security; Biometrics; Biometry - methods; Deep Learning; Female; Gram matrix conversion; Humans; liveness detection; Machine learning; Male; Neural networks; photoplethysmography (PPG) signals; Photoplethysmography - methods; Physiology; Signal Processing, Computer-Assisted; two-dimensional format; deep learning; real-time predictions; Gram matrix conversion; biometric security; liveness detection; classification; photoplethysmography (PPG) signals; two-dimensional format
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25010040

The integration of liveness detection into biometric systems is crucial for countering spoofing attacks and enhancing security. This study investigates the efficacy of photoplethysmography (PPG) signals, which offer distinct advantages over traditional biometric techniques. PPG signals are non-invasive, inherently contain liveness information that is highly resistant to spoofing, and are cost-efficient, making them a superior alternative for biometric authentication. A comprehensive protocol was established to collect PPG signals from 40 subjects using a custom-built acquisition system. These signals were then transformed into two-dimensional representations through the Gram matrix conversion technique. To analyze and authenticate users, we employed an EfficientNetV2 B0 model integrated with a Long Short-Term Memory (LSTM) network, achieving a remarkable 99% accuracy on the test set. Additionally, the model demonstrated outstanding precision, recall, and F1 scores. The refined model was further validated in real-time identification scenarios, underscoring its effectiveness and robustness for next-generation biometric recognition systems.