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.

Additional Metadata
Keywords Biometric, Electrocardiogram (ECG), Intra subject variability, Person identification, Wavelets
Persistent URL dx.doi.org/10.1109/TIM.2007.909996
Journal IEEE Transactions on Instrumentation and Measurement
Citation
Chan, A, Hamdy, M.M. (Mohyeldin M.), Badre, A. (Armin), & Badee, V. (Vesal). (2008). Wavelet distance measure for person identification using electrocardiograms. IEEE Transactions on Instrumentation and Measurement, 57(2), 248–253. doi:10.1109/TIM.2007.909996