## 4 Results and Discussion
The results of the single biometrics experiment using spectrogram and CNN-LSTM network are as shown in Table 5. The experiments were conducted correctly using all seventeen gestures from Ninaroro DB group 1. As a result of the experiment, twenty subjects showed an average of 99.17% accuracy, 99.76% recall, and 99.41% precision performance. Nine people (4 th, 5 th, 7 th, 8 th, 11 th, 12 th, 15 th, 19 th, and 20 th) were classified 100%, and the 14 th subject was classified 97.06%, showing the worst performance. The 14 th subject was most often misclassified as the 11 th subject, and the 9 th, 10 th, and 11 th gestures were most often misclassified, due to the similarity of the hand gestures. The results of a comparative experiment with the conventional single biometrics study using EMG signals are shown in Table 6. The EMG signal has a characteristic that shows different waveforms for each gesture when the same subject performs different gestures. Therefore, biometrics experiments with a large number of gestures degrade performance. The single biometrics method using EMG signals based on the CNN-LSTM network performed herein was tested with seventeen gestures in Ninaroro DB group 1. By comparison, most of the related studies conducted biometrics experiments using one gesture, and no study used more than six gestures. In addition, we showed 99.17% performance on twenty subjects, which was more subjects than in related works (except [12; 16]). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Gestures** & **Subjects** & **Accuracy (\%)** \\ \hline [PERSON] et al. [10] & 1 & 14 & 99.7 \\ [PERSON], [PERSON] et al. [11] & 3 & 5 & 81.6 \\ [PERSON] et al. [12] & 1 & 21 & 99.2 \\ [PERSON] et al. [13] & 3 & 8 & 94.6 \\ [PERSON] et al. [14] & 1 & 12 & 93 \\ [PERSON] et al. [15] & 5 & 6 & 47.66 \\ [PERSON], [PERSON] et al. [16] & 1 & 21 & 99.29 \\ [PERSON] et al. [17] & 1 & 10 & 98.2 \\ [PERSON] et al. [18] & 1 & 10 & 95.3 \\ Proposed & 17 & 20 & 99.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Single biometrics comparison experiment. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Gesture** & **Input Data Size (ea)** & **Accuracy (\%)** \\ \hline [PERSON] et al. [4] & 10 & 24 & 77.78 \\ [PERSON] et al. [5] & 5 & 136 & 86 \\ [PERSON] et al. [6] & 6 & 1600 & 94 \\ [PERSON] et al. [7] & 9 & 64 & 95.15 \\ [PERSON] et al. [13] & 3 & 1024 & 94.5 \\ [PERSON] et al. [8] & 7 & 3000 & 98.81 \\ [PERSON] et al. [9] & 10 & 7200 & 92 \\ Proposed & 17 & 60 & 83.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Gesture recognition comparison experiment. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fold** & **Accuracy (\%)** & **Recall (\%)** & **Precision (\%)** \\ \hline
1 & 98.68 & 99.85 & 98.83 \\
2 & 99.27 & 99.42 & 99.85 \\
3 & 99.56 & 100 & 99.56 \\ Average & 99.17 & 99.76 & 99.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of single biometrics experiments using the CNN-LSTM network. Figure 7 shows the 9 th, 10 th, and 11 th gestures as performed by the 11 th and 14 th subjects, which showed the highest misclassification rates in a single biometrics test. The Ninapro DB 9 th, 10 th, and 11 th gestures are EMG signals for performing 'wrist supination' operations, measured by varying axis and rotational direction. Therefore, we find that classification performance is low due to the use of EMG signals for similar hand gestures. The two-step biometrics method proposed herein performs single biometrics after gesture recognition, wherein data fusion of the two experimental results is performed in an AND form. This data fusion has an advantage in that the FAR can be lowered when it is performed in an AND form. Results of the comparison of FAR between two-step biometrics performing data fusion in an AND form and single biometrics is shown in Figure 8. As a result of the experiment, it was confirmed that the FAR of the two-step biometrics that was data fused in AND form was 0.0155%, which was 64.7% lower than that of single biometrics.