Lee, Jian Han
(2017)
A Comparative Study Of Different Types Of Mother Wavelets For Heartbeat Biometric Verification System.
Masters thesis, Universiti Sains Malaysia.
Abstract
Recently, advanced biometric technology is turning to the use of electrocardiograms (ECG) signal as new modality for verification system. The ECG signal contains sufficient information to verify an individual as it is unique to everyone. One of the feasible methods to extract the salient information from ECG signal for verification is by using wavelet transform. However, there is a challenge in implementing it as different types and orders of mother wavelet used will yield different verification performance. Therefore, in this study, a comparative study is done so as to investigate the optimum type and order of mother wavelet that represents the best feature for the verification system. Three different types of mother wavelets i.e. Symlet, Daubechies and Coiflet with order ranging from one to five have been studied in this research. The extracted features are then trained by using SVM classifier to generate a model to verify the features. The performance of the ECG biometric verification system is evaluated with the Receiver Operating Characteristic (ROC) plot and Equal Error Rate (EER). Experimental result showed that the developed system achieves the best performance when the 3rd order Coiflet is used as feature with an EER score of 10.755% is achieved.
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