Hear the Heartbeat in Phases: Physiologically Grounded Phase-Aware ECG Biometrics

TL;DR

This paper introduces a physiologically grounded, phase-aware ECG biometric framework that explicitly models cardiac phases and employs a multi-prototype enrollment strategy, achieving state-of-the-art identification accuracy.

Contribution

The paper proposes a novel Hierarchical Phase-Aware Fusion framework with a three-stage design and a multi-prototype enrollment strategy for improved ECG-based biometric recognition.

Findings

Achieves state-of-the-art results on three public datasets.

Effectively preserves phase-specific features for biometric recognition.

Reduces heartbeat variability impact through multi-prototype enrollment.

Abstract

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.