# Attention-Guided Fusion of 1D and 2D CNNs for Robust ECG-Based Biometric Recognition

arXiv: https://arxiv.org/abs/2605.17685 · HTML: https://mlsanity.com/paper/2605.17685/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 17 May 2026.

## Builds on (most cited first, 7 of 7)

- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review](https://mlsanity.com/paper/2301.03505/) (2023)
- [XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification](https://mlsanity.com/paper/2009.04796/) (2020)
- [ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation](https://mlsanity.com/paper/2204.03992/) (2022)
- [ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors](https://mlsanity.com/paper/2409.05627/) (2024)
- [ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention](https://mlsanity.com/paper/1812.07422/) (2018)
- [Classification of ECG based on Hybrid Features using CNNs for Wearable Applications](https://mlsanity.com/paper/2206.07648/) (2022)

## Abstract (arXiv)

Electrocardiogram (ECG)-based biometric recognition has emerged as a promising solution for secure authentication and liveness detection. However, most existing methods rely on unimodal deep learning architectures that independently process either one-dimensional (1D) temporal signals or two-dimensional (2D) time-frequency representations, limiting robustness and generalization. To address this issue, this paper proposes a hybrid framework integrating 1D and 2D convolutional neural networks (CNNs) within a unified end-to-end architecture. The 1D branch extracts temporal and morphological features from raw ECG signals, while the 2D branch captures discriminative spectral information from time-frequency representations. An attention-guided fusion mechanism dynamically weights both modalities according to input characteristics, overcoming the limitations of conventional static fusion strategies. The framework was evaluated on three benchmark datasets (ECG-ID, MIT-BIH, and PTB), including healthy subjects and patients with cardiac pathologies, achieving identification accuracies of 99.56%, 100.00%, and 99.89%, respectively. To assess long-term biometric permanence, experiments were also conducted on the multi-session Heartprint dataset spanning ten years. The proposed approach achieved same-session accuracies of 98.54% (S1), 99.09% (S2), 94.93% (S3R), and 96.08% (S3L), while cross-session evaluations reached 56.33% (S1-S2) and 53.27% (S2-S3R), demonstrating the ability to capture stable biometric signatures over time. The optimal configuration combines InceptionTime for 1D processing, ResNet-34 for 2D analysis, and attention-based fusion. Ablation studies confirm that the proposed attention mechanism consistently outperforms conventional fusion approaches. Overall, the proposed framework provides a robust, scalable, and high-performance solution for ECG biometric recognition.