ABSTRACT
The electrocardiogram has gained traction as a biometric trait due to its outstanding combination of universality, permanence, and measurability, with a hidden nature that makes it harder to steal or counterfeit. The state-of-the-art mostly consists of pipeline algorithms, composed of separate stages of de-noising, segmentation, feature extraction, and decision. However, convolutional neural Networks (CNNs), possess the tools to integrate all phases of processing, from acquisition to decision, in a single model. This integration replaces separate, step-by-step tuning with a holistic optimization process, synergically adapting the model to attain the best performance possible. This chapter, introduces and explores the capabilities of convolutional neural networks for biometric identification using non-intrusive ECG signal acquisitions, and proposes a CNN architecture for the complete integration of traditional pipeline stages in a single accurate and robust model. The method was evaluated on the highly complete and challenging U of TDB collection, and has shown promising results on identification tasks when compared with recent and successful state-of-the-art methods.
