Abstract—In recent years, Electrocardiogram (ECG) signals have emerged as a promising modality for biometric recognition, and have attracted significant attention. In this work, a new ECG biometric identification approach based on Spatial Pyramid Pooling (SPP) and Autoencoder (AE), referred to as the SPP-AENet, is proposed. The SPP-AENet is a hybrid architecture consisting of two parts: the SPP layer and AE layer. The SPP layer allows non-fixed length input and by incorporating prior knowledge, can ensure that critical characteristics of ECG signals are retained while compressing redundant information. The AE layer, through its automatic feature learning mechanism, can discover latent patterns and relationships within the data. The proposed method is evaluated using three public databases acquired under different health conditions, and gives a signal identification accuracy of 98.88%, 100%, and 100% for the ECG-ID, Physikalisch-Technische Bundesanstalt (PTB), and ST-Change database, respectively. To facilitate a better understanding of the model learning process, features of the SPP layer and the AE layer are visualized using t-distributed Stochastic Neighbor Embedding (t-SNE).
 
Keywords—Electrocardiogram (ECG) biometric, Spatial Pyramid Pooling (SPP), Autoencoder (AE), SPP-AENet, t-distributed Stochastic Neighbor Embedding (t-SNE)


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