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Biometric Recognition using Area under Curve Analysis of Electrocardiogram

In this paper, we introduce a human recognition system that utilizes lead I electrocardiogram (ECG). It proposes an efficient method for ECG analysis that corrects the signal and extract all major features of its waveform. FIR equiripple high pass filter is used for denoising ECG signal. R peak is detected using Haar wavelet transform. A novel class of features called as area under curve are computed from dominant fiducials of ECG waveform along with other known class of features such as interval features, amplitude features and angle features. The feasibility of an electrocardiogram as a new biometric is tested on selected features that reports the authentication performance 99.49% on QT database, 98.96% on PTB database and 98.48%on MIT-BIH arrhythmia database. The results obtained from the proposed approach surpasses the other conventional methods of biometric applications.