Total Variation PCA-Based Descriptors for Electrocardiography Identity Recognition

Electrocardiographic (ECG) signals have been successfully used in biometric recognition. However, the accuracy of ECG-based biometric systems is generally lower than systems based on other physiological traits. This study introduces a local feature learning method aimed at enhancing the performance of ECG-based biometric recognition systems. Specifically, we first extracted the multi-scale differential feature (MDF) for each point in the training ECG heartbeats using the difference between each point and its neighboring points. Second, we learn feature mapping to project these MDFs into low-dimensional descriptors in an unsupervised manner, where 1) the errors between the original MDF and reconstructed MDF are minimized. 2) The total variation in the reconstructed MDFs is minimized. Third, we represented each ECG heartbeat as a histogram feature using clustering and pooling descriptors. Finally, we adopted global feature learning methods to obtain a representation of an ECG heartbeat. Experiments on the MIT-BIH Arrhythmia, ECG-ID, and Physikalisch Technische Bundesanstalt databases verified the performance of the proposed method over existing ECG biometric recognition methods using within-session analysis. Moreover, we evaluated the performance of the proposed method using an across-session analysis of the ECG-ID database.