Evaluating Multimodal Physiological Signals for Biometric Human Recognition Using GSR, ECG, and PPG Signals

In recent years, physiological signal-based biometrics has gained increasing attention due to its resistance to spoofing attacks, suitability for continuous authentication, and compatibility with wearable devices. This study investigates a multimodal biometric framework based on three physiological signals: galvanic skin response (GSR), electrocardiogram (ECG), and photoplethysmography (PPG). Although GSR has demonstrated promising performance in emotion recognition and animal recognition studies, it remains relatively underexplored in biometric human recognition applications compared with ECG and PPG. As a non-invasive signal that can be easily acquired through simple skin contact, GSR offers several advantages, including low-cost sensing, ease of integration into wearable devices, user convenience, and suitability for continuous monitoring. To address this gap, the proposed framework combines features extracted from ECG, PPG, and GSR signals and employs machine learning algorithms for human recognition. The approach was evaluated using two publicly available datasets, CLAS and MAUS. Experimental results demonstrate that multimodal fusion significantly enhances recognition performance, achieving accuracies of 97% and 99% on the CLAS and MAUS datasets, respectively, with the K-Nearest Neighbors (KNN) classifier. These findings highlight the potential of integrating GSR with ECG and PPG signals to develop biometric systems for wearable and continuous authentication applications.