AI RESEARCH
Attention-Guided Fusion of 1D and 2D CNNs for Robust ECG-Based Biometric Recognition
arXiv CS.CV
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ArXi:2605.17685v1 Announce Type: new 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.