Simple yet Effective ECG Identity Authentication With Low EER and Without Retraining

Recently, electrocardiogram (ECG) signals have garnered significant attention in the field of identity authentication. For identity authentication, the ECG signals collected by wearing smart devices need to be determined whether the signal belongs to an enrolled one. Constrained by the computational efficiency of smart devices in practical scenarios, it is essential to reduce the complexity of the method to lower the computational load. To maintain the accuracy (ACC) of identity authentication, most research efforts rely on both R‐wave extraction and segmentation for subsequent authentication. Moreover, many methods constantly require the model to be retrained during the user enrollment stage, leading to performance degradation and waste of training resources. Hence, we propose a simple yet effective ECG identity authentication method that applies blind segmentation and is free from retraining, which greatly simplifies the authentication process. To mitigate the equal error rate (EER) during the verification phase, a combination of AAM‐softmax and triplet losses is employed, along with the incorporation of the hard negative mining within batch samples. Extensive experiments demonstrate that our method outperforms competitors by a large margin, e.g., achieving 0.40% EER on the large‐scale autonomic dataset. Within models of comparable parameter sizes, our approach demonstrates markedly higher computational efficiency on both CPU and GPU platforms. The source code has been publicly released and is available at: https://github.com/DanMerry/LowEER .