Robust ECG Biometrics Using Two-Stage Model

Bo Wu, Gongping Yang, Lu Yang, Yilong Yin

Abstract: ECG biometrics has achieved great success on high quality ECG signals. However, it is still a challenging problem to apply ECG biometrics on mobile devices due to the low quality signals. In this paper, we propose a robust two-stage model. In first stage, we utilize 1D CNN model to remove the invalid heartbeats from ECG recording. And then, we combine the raw signal with the hidden feature of 1D CNN as the feature representation of heartbeat. In second stage, we group a certain number of heartbeat representations as input sequence. Attention-based bidirectional LSTM is used to aggregate input sequence and generate discriminative identity features for recognition. We evaluate our method on two public datasets, and the results show that our two-stage model can achieve the state-of-the-art performance compared with other existing methods.