ECG Biometrics Based on Attention Enhanced Domain Adaptive Feature Fusion Network

In recent years, there has been an increasing focus on privacy and security among individuals. Biometric systems are highly regarded for their strong resistance to counterfeiting. Among various biometric features, ECG signals are difficult to falsify and are less attack-prone. However, as the time interval between signal acquisitions increases, the dissimilarities between individual ECG signals become more pronounced, making it difficult for many studies to achieve satisfactory recognition results in multi-session recognition. Furthermore, some studies encountered challenges in extracting crucial features from the ECG signal, which posed difficulties for identification experiments. To address the above challenge, this study proposes a novel attention-enhanced domain adaptive feature fusion network. Firstly, the network employs a multi-branch architecture to extract essential features from various dimensions of the ECG signal. Secondly, it incorporates the proposed weight fusion adaptive attention mechanism to further emphasize the features of heartbeats that contribute to recognition. Additionally, domain adaptive technology is employed to mitigate differences in feature distribution of ECG signals across sessions, thereby enhancing the model's generalization capability. Finally, four well-known databases, the ECG-ID database, PTB database, CYBHi database, and Heartprint database, were utilized to evaluate the performance of the model. These databases predominantly comprise individuals with multiple records, fulfilling the prerequisites for multi-session recognition. The model achieved recognition results of 96.31%, 73.79%, 77.80%, and 54.78% on these four databases, respectively, surpassing related studies in multi-session recognition scenarios.