Human recognition using Fisher׳s discriminant analysis of heartbeat interval features and ECG morphology

Abstract

The author of this paper presents a novel method to use the electrocardiogram (ECG) as a biometric for human recognition. The ECG is a physiological signal that links to the life signs of an individual and does not need vitality testing. Thus, ECG biometric system has potential to prevent the fraudulent attacks. The hybrid approach consisting analytical and appearance methods is used to derive the ECG features. In order to make the method insensitive to signal variations and muscle flexure, the ECG features are linearly projected using Fisher׳s discriminant method. The method selects heartbeat features of lower dimension in the Fisher space that have sufficient discriminatory information between inter-subject ECG signals. The experiment shows that the proposed ECG biometric method achieves the equal error rates (EER) of 0.76% and 0.71% in recognizing people suffering from cardiac arrhythmia and people of good health, respectively. On mixed health statuses, the method achieves an EER of 1.31% confirming a very good performance and robustness of the proposal.