LAUSR

Studies have been actively conducted on biometrics technology applying electrocardiogram (ECG) signals, which are more robust against forgeries and alterations than fingerprint and face authentication. The ECG lead-I signals measured using ECG acquisition devices consist of 1D data. Therefore, it has limitations with regard to feature extraction and data analysis. This paper proposes a user-recognition system that extracts multi-dimensional features through 2D resizing based on bi-cubic interpolation, which improves the calculation speed and preserves the original data values after converting the measured ECG into a spectrogram. An ECG measuring device was developed, and the ECGs were measured using the developed device. The proposed system consists of an ECG acquisition step, an ECG signal processing step, a segmentation step, a feature extraction step, and a classification step. For ECG signals, the CU-ECG dataset was created by acquiring ECG lead I signal data from 100 subjects in a relaxed state for a period of 160 s. For three sets of shuffle classes that applied the CU-ECG dataset, the average recognition performance was 93% for the existing algorithm and 88.9% for the parameter adjustment method. The average recognition performance of the proposed user recognition system showed a 0.33% improvement compared to the existing algorithm and a 4.43% improvement compared to the parameter adjustment method.