Person Identification using Electrocardiograms

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 3 data recordings 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 the wavelet transform. The wavelet distance measure has a classification accuracy of 95%, outperforming the other methods by over 10%. This ECG person identification modality would be a useful supplement for conventional biometrics, such as fingerprint and palm recognition systems.

• Type: Conference
• Year: 2006
• Where: CCECE
• Authors: Adrian D. C. Chan, Mohyledin M. Hamdy, Armin Badre, Vesal Badee
• Keywords: CCECE 2006, Data Recordings Sessions, Distance Measure, ECG Data, Electrical And Computer Engineering