Online ECG Biometrics for Streaming Data with Prototypes Learning and Memory Enhancement.

Recently, electrocardiography (ECG) has attracted significant attention in the field of biometrics, presenting a compelling alternative for biometric recognition based on physical or biological traits. Impressive application results have been achieved by existing methods, the majority of which are designed in the batch processing mode. The batch mode inherently assumes that all data can be acquired prior to training the final model and that no new data will subsequently arrive. Clearly, this assumption is unrealistic, as real-world data often arrive in a streaming fashion, meaning that they are continuously generated and transmitted. When confronted with streaming data, traditional batch-based methods require re-training on all the data once again, including both the newly arrived data and the previously trained data. Consequently, these methods lead to redundant calculations and significant expenses. To overcome this limitation, we propose a new online method for ECG biometrics that incrementally learns from streaming data. Our method updates itself with only the new arriving data, eliminating the need to retrain with both old and new data. To enhance the discriminative power of to-be-learned sample representations, we introduce two novel modules: bidirectional regression and prototype learning. Since our method does not revisit old data when new data arrive, we incorporate a memory enhancement module to mitigate the catastrophic forgetting problem caused by a lack of exposure to old data. Furthermore, we design a novel and efficient online optimization algorithm to minimize the overall loss function. Extensive experiments conducted on two widely used datasets demonstrate the effectiveness of our proposed method.