Photoplethysmogram (PPG)-Based Biometric Identification Using 2D Signal Transformation and Multi-Scale Feature Fusion

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 15; p. 4849
• Main Authors: Xu, Yuanyuan, Wang, Zhi, Liu, Xiaochang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07.08.2025; MDPI
• Subjects: 2D signal transformation; Accuracy; Algorithms; Biometric identification; Biometric Identification - methods; biometric recognition; Biometrics; Datasets; Deep learning; Facial recognition technology; feature fusion; Heart rate; Humans; Machine learning; Neural networks; Personal information; photoplethysmography; Photoplethysmography - methods; Physiology; residual neural network; Signal Processing, Computer-Assisted; Wavelet Analysis; biometric recognition; feature fusion; photoplethysmography; 2D signal transformation; residual neural network
• DOI: 10.3390/s25154849
• ISSN: 1424-8220

Using Photoplethysmogram (PPG) signals for identity recognition has been proven effective in biometric authentication. However, in real-world applications, PPG signals are prone to interference from noise, physical activity, diseases, and other factors, making it challenging to ensure accurate user recognition and verification in complex environments. To address these issues, this paper proposes an improved MSF-SE ResNet50 (Multi-Scale Feature Squeeze-and-Excitation ResNet50) model based on 2D PPG signals. Unlike most existing methods that directly process one-dimensional PPG signals, this paper adopts a novel approach based on two-dimensional PPG signal processing. By applying Continuous Wavelet Transform (CWT), the preprocessed one-dimensional PPG signal is transformed into a two-dimensional time-frequency map, which not only preserves the time-frequency characteristics of the signal but also provides richer spatial information. During the feature extraction process, the SENet module is first introduced to enhance the ability to extract distinctive features. Next, a novel Lightweight Multi-Scale Feature Fusion (LMSFF) module is proposed, which addresses the limitation of single-scale feature extraction in existing methods by employing parallel multi-scale convolutional operations. Finally, cross-stage feature fusion is implemented, overcoming the limitations of traditional feature fusion methods. These techniques work synergistically to improve the model’s performance. On the BIDMC dataset, the MSF-SE ResNet50 model achieved accuracy, precision, recall, and F1 scores of 98.41%, 98.19%, 98.27%, and 98.23%, respectively. Compared to existing state-of-the-art methods, the proposed model demonstrates significant improvements across all evaluation metrics, highlighting its significance in terms of network architecture and performance.