Key result
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.
May facilitate ECG biometrics on mobile sensors; leaves open prospective validation in clinical populations.
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.
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Choi et al. (2016) studied 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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