Hear the Heartbeat in Phases: Physiologically Grounded Phase-Aware ECG Biometrics
This work improves biometric authentication for wearable device users by incorporating physiological phase awareness, though it is incremental as it builds on existing ECG methods.
The paper tackles the problem of ECG-based identity authentication by addressing the oversight of phase-specific characteristics within heartbeats, proposing a Hierarchical Phase-Aware Fusion (HPAF) framework and a Heartbeat-Aware Multi-prototype (HAM) enrollment strategy, which achieve state-of-the-art results on three public datasets under closed and open-set settings.
Electrocardiography (ECG) is adopted for identity authentication in wearable devices due to its individual-specific characteristics and inherent liveness. However, existing methods often treat heartbeats as homogeneous signals, overlooking the phase-specific characteristics within the cardiac cycle. To address this, we propose a Hierarchical Phase-Aware Fusion~(HPAF) framework that explicitly avoids cross-feature entanglement through a three-stage design. In the first stage, Intra-Phase Representation (IPR) independently extracts representations for each cardiac phase, ensuring that phase-specific morphological and variation cues are preserved without interference from other phases. In the second stage, Phase-Grouped Hierarchical Fusion (PGHF) aggregates physiologically related phases in a structured manner, enabling reliable integration of complementary phase information. In the final stage, Global Representation Fusion (GRF) further combines the grouped representations and adaptively balances their contributions to produce a unified and discriminative identity representation. Moreover, considering ECG signals are continuously acquired, multiple heartbeats can be collected for each individual. We propose a Heartbeat-Aware Multi-prototype (HAM) enrollment strategy, which constructs a multi-prototype gallery template set to reduce the impact of heartbeat-specific noise and variability. Extensive experiments on three public datasets demonstrate that HPAF achieves state-of-the-art results in the comparison with other methods under both closed and open-set settings.