Evaluation of Biometric Template Permanence for Electrocardiography (ECG) Based User Identification in Sanitary Facilities

In our previous work, we explored a new invisible ECG biometrics approach that uses signals collected at the thighs using polymeric dry electrodes and sensors integrated into a toilet seat. However, the performance of the biometric templates remains unexplored. In this paper we evaluate how the ECG templates evolve, and the impact that potential changes may have on performance, using one case-study subject monitored over 31 days. This work is organized into two main parts. The first explores the morphological and physical traits of the subject throughout the 31 days based on data collected daily, three times per day at 6-hour intervals; in more than 80% of the sessions, all the signals were successfully acquired without showing noise nor movement artefacts. The second part is focused on evaluating the performance of Support Vector Machine (SVM) and Binary Convolutional Neural Network (BCNN) classifiers in the identification of the case study subject within a population of 10 individuals, covering an age range of (24 to 35 years); the top performer was the BCNN, achieving a perfect accuracy rate of 100% when tested on a group of two individuals.