Method of human identification using ECG signals based on features extracted frrom wavelet and curvelet domains

Abstract

Prevention of forged identities via human identification is crucial for any authentication system. Electrocardiogram (ECG) is the graphical representation of the heart surface potential as a function of time. ECG is an emerging Biometric trait which can overcome the limitation of forgery of traditional Biometrics. ECG based system can detect the presence of person in living form and thus proves the aliveness of that individual. For ECG based human identifi cation, apart from template matching approach, methods involving features categorized as non-fiducial and fiducial have been reported in the literature. Although, some of these methods offer high identification accuracy, methods capable of highly accepting heartbeats authentic to the training database and that of truly rejecting heartbeats external to the training database thus providing higher authentication accuracy, have been limitedly reported. Therefore, the development of an ECG based human identification method capable of effectively identifying true identity remains a challenging task. Exploiting the fact that approximate coefficients of discrete wavelet transform (DWT) of ECG signal contain the low frequency components which provide intrinsic varying details from person to person thus can be employed as a feature in human identification. Considering the system pole preserving property of autocorrelation, approximate coefficients of DWT employed on the autocorrelation sequence of ECG signal is found to be more eff ective than that obtained from the ECG signal. Unlike the DWT coefficients, the discrete curvelet transform coefficients have directional p a r a m e t e r s and are more efficient in representing curve-like edges that differ in ECG signals of diff erent per- sons. Therefore, cross-correlation of adjacent columns and mean of column elements of discrete curvelet transform coefficients are utilized to form sets of features that can offer even a better attractiveness in identifying different humans. In order to reduce feature dimension, cross-correlation and MC features are reduced based on dominant energy bands and by employing PCA. The proposed feature sets when fed to each of the euclidean distance based classifier are shown to able to identify humans with higher authentication and identification accuracy. Evaluation of simulation results indicate that the proposed methods provide superior efficacy compared to that obtained from some of the state-of-the-art methods of human identification.