## 1 Introduction  
Biometrics refers to the recognition of individuals based on physiological or behavioral characteristics [1]. Representative biometric traits include the face, fingerprints, retina, iris, and voice. Various methods use these information sources to recognize individuals [2; 3; 4]. These traditional identification technologies have limitations, such as a limited scope recognition, as well as vulnerability to loss and duplication. Therefore, for systems that require higher security, studies of the distinct biometric features of the principal subject to verification are actively being conducted. Electrocardiogram (ECG) involves information about the structural and functional cardiac muscle activities, and it is a simple and effective representative of a noninvasive diagnostic method. Every individual has characteristic ECG features, such as universality, uniqueness, and liveness detection. Such signals provide strong protection against forgery [5]. However, in biometric recognition using ECG, signal irregularities may exist due to the individual's illness, and the procedure could be hampered by difficulties, such as a long waiting time for data collection [6; 7; 8]. Nevertheless, studies on ECG biometrics can be used in extensive fields, such as commercial environments, security, health management, and systems like smart cards [9]. Thus, it is necessary to enhance the accuracy of recognition. As a biometric study using ECG, fiducial point-based studies were performed as follows: [PERSON] et al. [10] suggested ECG-based recognition system using temporal features. After removing the noise in an ECG signal, P wave, QRS complex, and the T wave were detected and by extracting 15 fiducial point features, linear discriminant analysis (LDA) was used for classification. As a result of experimenting with 29 subjects, the results indicated a 100% subject identification rate and an 82% ECG beat identification rate. [PERSON] et al. [11] extracted 30 fiducial point features of ECG signals and then used soft independent modeling of a class analogy (SIMCA) classifier to compare the unlabeled data with the data of a trained group, and suggested a method of classification into the most well-matched class. A test experiment was conducted on 20 subjects in total, which demonstrated a 100% recognition rate. [PERSON] et al. [12] suggested a method of combining the time between ECG beats, amplitude features, and R wave features. Fiducial point detection was performed to measure the time and waveform distance in the pre-processed ECG signal and principal component analysis (PCA), or LDA was used to extract the morphological features. In case that the two types of features were combined for performance, there was a 100% subject identification rate and a beat identification rate of 99.43%; 98.90% were identified with regard to the 13 subjects of the NSR and PTB database (DB), respectively. However, the problem with the fiducial-based algorithm is that it misses the morphological features of ECG signals. In addition, these features may increase the overall complexity of a biometric system due to the lack of universally accepted rules for the precise detection of the fiducial points for ECG waveforms [13; 14]. In the detection of R peaks among various ECG waveforms, the detection criteria for waveforms other than those of R peaks are not clearly defined [15; 16]. Non-fiducial point-based studies have been conducted in order to overcome the disadvantages of the fiducial point method. Unlike the fiducial point-based method, the non-fiducial approach-based method does not require precise boundaries of the waveforms. It extracts differential data among ECG waveforms without obvious fiducial points. As a non-fiducial point-based study, several studies were performed as follows: [PERSON] et al. [17] proposed a non-fiducial method of ECG recognition using a band pass filter with a 1-30 Hz frequency for the ECG signal to remove the noise, applying an n-bit uniform quantization and using a string similarity measure.