## 1 Introduction  
Biometrics refers to the verification of identity using an individual's behavioral and physical features. Currently, widely used biometrics include face, fingerprint, and iris recognition as physical features. However, faces, fingerprints, and irises are bodily information without liveness that can be forged using 3D printers, lenses, and silicon, raising security concerns [1]. To compensate for this issue, biometrics studies using biosignals, which are a behavioral feature, are being conducted. Biosignals are signals that measure the flow of microcurrents generated through biological activities, which cannot be replicated outside the body [2]. In addition, biosignals are signals with liveness, thereby having strong security advantages as they cannot be forged and falsified [3]. Examples of biosignals include electromyogram (EMG), electrocardiogram (ECG), and electroencephalogram (EEG) signals. Among them, the EMG signal represents a signal obtained in the form of a voltage value by measuring a microcurrent generated whenever there is a muscle contraction. There are two methods of measuring EMG signals: a needle electrode method and a surface electrode method. The needle electrode method measures an action potential occurring at a point in the muscle by inserting a needle electrode into a muscle. The surface electrode method measures an action potential by placing electrodes to the skin surface. Measurement of the EMG signal using the surface electrode method wherein an electrode is placed to the skin surface is shown in Figure 1[1]. In terms of the electrode placement position, the signal is measured by placing the electrode at a muscle which change during experimentation or a muscle position that is frequently used in existing studies.  
Biometrics studies using EMG signals are lacking compared to studies using ECG and EEG signals. However, the EEG signal gets distorted as it passes through the skull, which is a disadvantage while measuring the signal, and the ECG signal is a periodic signal, which is a drawback by itself in that the waveform of the signal cannot be changed if the registered data is hacked. By comparison, the EMG signal can be measured more easily than the EEG signal, and its registration data can be changed distinctly, unlike the ECG signal, using a feature representing a different waveform of a signal for each gesture. EMG signals in existing works have been used as gesture recognition or single biometrics.  
In this paper, we proposed a method for using EMG signal-based gesture recognition and single biometrics together. The proposed method is a two-step biometrics method that uses CNN-LSTM network-based EMG signals. The time domain characteristics of the EMG signal and the LSTM network were used to determine whether the user's gesture matches. Single biometrics was performed using the spectrogram and CNN-LSTM network if it matched the registered gesture. Data fusion of gesture recognition and single biometrics was performed in the form of AND in which the false acceptance rate (FAR) was lowered. As a result of the experiment, it was confirmed that the FAR decreased by 64.7%. The remainder of this paper is structured as follows. Existing gesture recognition and biometrics studies using bioinformation are mentioned in Section 2. A two-step biometrics method using EMG signals based on the proposed CNN-LSTM network is described in Section 3. The results of the two-step biometrics experiment using our proposed method are explained in Sections 4 and 5 is the conclusion.