Key result
A bi-modal cardiac biometric recognition system using ECG and PCG signals demonstrated promising recognition performance when tested over 21 subjects.
Cardiac signals (ECG and PCG) show promising applicability for biometric recognition using wavelet-based analysis and bi-modal fusion.
May enable cardiac signals for biometrics; leaves open validation in larger, diverse cohorts before any application.
This letter examines the applicability of cardiac signals for biometric recognition. Two physiological signals are considered, namely the Electrocardiogram (ECG) and the Phono-cardiogram (PCG) as it has been shown they bare adequate discriminative information in a population. Due to the idiosyncratic properties of ECG and PCG, individual algorithms are developed for feature extraction. Time dependency, a major challenge of cardiac biometrics, is taken to consideration in the design of robust gallery templates. To that end, a wavelet based analysis is introduced to handle noise artifacts and heart rate variability. A bi-modal configuration is presented, to perform decision level fusion of the information. The recognition performance, tested over 21 subjects, is very promising.
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Fatemian et al. (2010) conducted a letter in Biometric recognition (n=21). ECG and PCG biometric recognition was evaluated on Recognition performance. A bi-modal cardiac biometric recognition system using ECG and PCG signals demonstrated promising recognition performance when tested over 21 subjects.
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