## 3 Materials and Methods
### Single Biometrics Method Using EMG Signals  
The single biometrics method among the two-step biometrics methods proposed herein used a spectrogram and the CNN-LSTM network. The time domain of the EMG signal represents information on muscle activity, and the frequency domain has the advantage of being immune to noise [1]. Therefore, the one-dimensional EMG signal was converted into a two-dimensional spectrogram to simultaneously analyze time-frequency domain information. The spectrogram was generated using a window function of a fixed length within the time domain of a given signal. In the generated spectrogram, the horizontal axis represented time, and the vertical axis represented frequency information.  
In the case of biometrics using the existing EMG signal, a handcrafted method is performed wherein a subject extracts features by using a specific feature extraction algorithm. However, this method lacks versatility and has a problem in that it may not be able to extract features optimized for a signal. To solve this problem, deep learning methods such as CNN and LSTM networks, which have been actively studied recently, can automatically extract optimal features from data without extracting features via the handcrafted method [28].  
A CNN-LSTM network was designed herein to perform single biometrics using EMG signals as shown in Figure 6. In this paper, a single biometrics was performed using a network of three convolutional layers, three pooling layers, two LSTM layers, and two fully connected layers. Batch normalization was performed between each convolutional layer and the pooling layer, and a dropout (0.3 probability) was placed after three convolutional layers. The layer placement and parameters of the network were selected through experiments, and a rectified linear unit (ReLU) was used as the activation function. Network learning was done by setting sgdm optimizer, batch size 50, and epoch with 100. The spectrogram input to the network was normalized to 256 \(\times\) 256, and the output data size, padding, stride, and number of filters used in the experiment are shown in Table 2. The stride and padding were set to 1 and 2, respectively, to ensure that the data size remained unchanged in the convolutional layer, and the stride and padding were set to 2 and 0, respectively, to reduce the data size in the pooling layer.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer** & **Input Size** & **Output Size** & **Stride** & **Padding** & **Filter Count** \\  & & & & & **(Hidden Unit)** \\ \hline Input & - & 256 \(\times\) 256 \(\times\) 3 & - & - & - \\ Conv1 & 256 \(\times\) 256 \(\times\) 3 & 256 \(\times\) 256 \(\times\) 20 & 1 & 2 & 20 \\ Pool1 & 256 \(\times\) 256 \(\times\) 20 & 128 \(\times\) 128 \(\times\) 20 & 2 & - & - \\ Conv2 & 128 \(\times\) 128 \(\times\) 20 & 128 \(\times\) 128 \(\times\) 20 & 1 & 2 & 20 \\ Pool2 & 128 \(\times\) 128 \(\times\) 20 & 64 \(\times\) 64 \(\times\) 20 & 2 & - & - \\ Conv3 & 64 \(\times\) 64 \(\times\) 20 & 64 \(\times\) 64 \(\times\) 20 & 1 & 2 & 20 \\ Pool3 & 64 \(\times\) 64 \(\times\) 20 & 32 \(\times\) 32 \(\times\) 20 & 2 & - & - \\ LSTM1 & 20,480 & 1024 & - & - & 1024 \\ LSTM2 & 1024 & 1024 & - & - & 1024 \\ FC1 & 1024 & 512 & - & - & - \\ FC2 & 512 & 20 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Designed CNN-LSTM network parameters.  
Figure 6: CNN-LSTM network structure for single biometrics.