An ECG authentication procedure using a cascading bandpass filter and radial basis function kernel-based SVM achieved an equal error rate of 1.87% on 15-second testing time among 175 subjects.
Low-cost mobile ECG sensors can be effectively used for biometric authentication using a cascading bandpass filter and SVM classifier.
Electrocardiogram (ECG) signals from mobile sensors are expected to increase the availability of authentication in the emerging wearable device industry. However, mobile sensors provide a relatively lower quality signal than the conventional medical devices. This paper proposes a practical authentication procedure for ECG signals that collected via one-chip-solution mobile sensors. We designed a cascading bandpass filter for noise cancellation and suggest eight fiducial features. For classification-based authentication, we use the radial basis function kernel-based support vector machine showing the best performance among nine classifiers through experimental comparisons. In spite of noisy ECG signals in mobile sensors, we achieved 4.61% of the equal error rate (EER) on a single heartbeat, and 1.87% of EER on 15 s testing time on 175 subjects, which is a reasonable result and supports the usability of low-cost ECGs for biometric authentication.
Choi et al. (Fri,) conducted a other in Biometric authentication (n=175). Radial basis function kernel-based support vector machine with cascading bandpass filter vs. Other classifiers was evaluated on Equal error rate (EER). An ECG authentication procedure using a cascading bandpass filter and radial basis function kernel-based SVM achieved an equal error rate of 1.87% on 15-second testing time among 175 subjects.
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