## 1 Introduction
[16] presented a method that uses a list of gestures as a password for EMG user authentication system for mobile phones access. Fourier transform was used to extract the features from the EMG signals. [PERSON] et al. [17] presented a method for improving EMG utilization, which was based on biometrically identifying a specific user. The experiments were performed to identify small group sizes of four, 10, and 19. An average identification accuracy across all 11 gestures was found to be 55.32%, 75.44%, and 90.32%, for groups of 19, 10, and four subjects, respectively. [PERSON] et. al. [18] used eight dry sensors to measure EMG from the wrist and carry out a personal authentication approach. A convolutional neural network (CNN) was used in the learning phase for authentication. The data collected by the authors were from eight individuals over a four-day period resulting in a total of 960 data captures. The average accuracy of the two-class separation was 94.9% by CNN [18]. The development and optimization of sEMG feature extraction and classification for the purpose of controlling prostheses or use in biometric applications is an active area of research, even though the analysis performed is mainly from a machine learning perspective [19; 20; 21]. Feature extraction involves the production of specific data structures from the raw data after eliminating noise and stressingimportant signal features. In general, features extraction can occur in the time domain, frequency domain, or the combined time-frequency domain [22]. The steps in analyzing the EMG signals have been proposed by [PERSON] et al. [23]. Initially, the surface EMG signal is obtained from the user's forearm while using discrete wavelet transform. Subsequently, singular value decomposition is used for feature extraction. Furthermore, fuzzy logic classifiers are used to recognize the different hand gestures in the context of linguistic terms. [PERSON] et al. [24] compared five feature extractions from an eight channel EMG signal that was obtained while using a wearable bracelet located on the forearm muscles resulting in significant differences in hand gestures. The time series features extraction that were evaluated by the authors were mean absolute value (MAV), willison amplitude (WAMP), variance (VAR), zero crossing (ZC), and waveform length (WL). MAV and WL are found to offer better recognition rate. [PERSON] et al. [25] acquired EMG signals from BIOPAC system. Subsequently, seven frequency domain features were extracted and then classified using a radial basis function (RBF) network. The average accuracy was 80%. [PERSON] et.al. [26] proposed a method that uses a unique combination of gestures as a password. The author's demonstrated the similarities and differences between the patterns and behaviours of the gestures obtained from different subjects [26]. To identify pass-gestures, four features were extracted in the time domain, including the maximum and minimum value of unprocessed s-EMG data and their corresponding time-min and time-max. Support vector machine (SVM) classifiers of each user were trained on separating these four features, where cross validation was performed using the same raw data. This paper proposes biometrics authentication system for user's verification. The biometric identity studied in this research is based on the EMG signal. The biometric device used to acquire the sEMG signal is a wearable multi-channel armband consisting of eight electrodes. A total of 56 users were enrolled in the biometric system. The users enrolled trained to use the sEMG biometric system prior to data collection. A total of 18 features were extracted from the signals to distinguish between users. Seven frequency domain features and eleven-time domain features were analyzed. Initially, the power spectral density (PSD) of each channel was estimated using the periodogram function implementing [PERSON]'s method.