A novel wavelet distance measure for ECG-based person identification achieved a classification accuracy of 89%, outperforming percent residual difference and correlation coefficient by nearly 10%.
A novel wavelet distance measure for ECG-based person identification achieved 89% accuracy, suggesting ECG could be a useful supplement to conventional biometrics.
In this paper, the authors present an evaluation of a new biometric based on electrocardiogram (ECG) waveforms. ECG data were collected from 50 subjects during three data-recording sessions on different days using a simple user interface, where subjects held two electrodes on the pads of their thumbs using their thumb and index fingers. Data from session 1 were used to establish an enrolled database, and data from the remaining two sessions were used as test cases. Classification was performed using three different quantitative measures: percent residual difference, correlation coefficient, and a novel distance measure based on wavelet transform. The wavelet distance measure has a classification accuracy of 89%, outperforming the other methods by nearly 10%. This ECG person-identification modality would be a useful supplement for conventional biometrics, such as fingerprint and palm recognition systems.
Chan et al. (2008) studied this question. Wavelet distance measure for ECG-based person identification vs. Percent residual difference and correlation coefficient was evaluated on Classification accuracy. A novel wavelet distance measure for ECG-based person identification achieved a classification accuracy of 89%, outperforming percent residual difference and correlation coefficient by nearly 10%.
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