I-vector Evaluation of Electrocardiogram (ECG) Biometric Identification System based on Sequential Compensation Approach

Abstract

Abstract In the past decade, i-vector shows promising result in speaker recognition by modelling a total variability subspace. In this study, as ECG is analogous to speech signals, we focused on the performance of i-vector models based on various compensation methods for ECG identification system in order to combat variability issues. The performance of the models is evaluated based on unbiased protocol (protocol 1) and all-subject protocol (protocol 2) for different compensation methods, including whitening, Linear Discriminate Analysis (LDA), Within-Class Covariance Normalization (WCCN) for single approach, and WCCN-whitening, LDA-whitening, WCCN-LDA for sequential approach. ECG-ID database from Physionet is used for evaluation. It contains 90 subjects with 310 ECG recordings. From the experimental results, we observed that sequential approach has overwhelmed the single approach. i-vector with WCCN-LDA model showed the best rank-1 performance among all the compensation methods for both protocols, 91.89% and 88.89% respectively. In contrast, LDA achieved 67.11% and 64.44% for protocol 1 and 2 respectively, which is the worst among others. We also observed that, the sequential approach requires lesser Gaussian components, consistently 16 components compared to single compensation techniques which requires 32 or more components to achieve the best result hence this shortens the system computation time.