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This paper examines the applicability of cardiac signals for biometric recognition. Two physiological signals are considered, namely the Electrocardiogram (ECG) and the Phonocardiogram (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.
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