Abstract
Dealing with signal and session variability is a common problem in biometric recognition system since biometric signal is frequently inconsistent over time. Health, aging, emotional conditions and different recording settings are some of the factors that contribute to the variability issue. This cause the two samples of the same subject tends to be different from each other hence giving a mismatch effect between the enrolments and testing condition. Over the years, solving the variability problem by subspace representation concept has become prevalent. Hence, it motivates us to validate a recognition algorithm based on factor analysis perspective and we use electrocardiogram (ECG) signal for our experimental data as it is subject to change over time and sensitive to different sensors. We first model each supervectors extracted from Gaussian Mixture Model (GMM) into two different factors which are subject and session independent supervectors based on Joint Factor Analysis (JFA) algorithm. For the second model which is based on i-vector approach, the supervectors extracted from GMM is first modelled to be a single total factor and a compensation method is then employed to compensate the variability effect. Three compensation methods for the i-vector are employed which are Probabilistic Linear Discriminate Analysis (PLDA), Linear Discriminate Analysis (LDA) and Within Class Covariance Normalization (WCCN). The ECG-ID database obtained from physionet database consists of 90 subjects with a total of 310 ECG recordings; each recorded for 20 seconds are used in this study. Experimental results reveal the robustness of the i-vectors PLDA approach by giving 2.156% and 2.155% of Equal Error Rate (EER) for protocol 1 and 2, respectively.
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