# Skeleton-Based Dynamic Hand Gesture Recognition Using an Enhanced Network with One-Shot Learning
## 5 Results and Discussion  
To visualize the process of the recognition accuracy measured on the validation set, we have separately analyzed two different ways of 14-classes: one finger and the whole hand, and the 28-classes encompassing both the above two ways. In addition, the accuracy curve is shown in Figure 6.  
From Figure 6, the 14-classes, (1) represents right-hand gestures performed with one finger, and (2) represents gestures with the whole hand. The curve of the one-finger classified by our method is shown in blue, the curve of the whole-hand is shown with an orange line, and the curve of the 28-classes is shown with a grey line. It is observed that the recognition accuracy of the 14-classes (2) is superior to the 14-classes (1), and the 28-classes is between those two. Compared with the 14-classes (1), the 28-classes has better performance. To assess the effectiveness of our algorithm for classifying the hand gestures of DHGD into 14-classes and 28-classes, we compare the standard LSTM network with regard to their DHGD recognition accuracy. Table 2 shows the comparison results of skeleton-based hand gesture recognition between LSTM and GREN networks. From Table 2, the final accuracy of our GREN network reaches 82.29% for the 14-classes classification that is the average of the two ways and 82.03% for the 28-classes classification. The proposed network indicates that recognition accuracy can reach 78.65% for the one-finger and 85.90% for the whole-hand. Thus, compared with the standard LSTM networks, the accuracy of the recognition increased by approximately 5.14%, the accuracy of the one-finger increased by approximately 3.47%, and the whole-hand accuracy increased by 6.08%, which show excellent performance of our method in one-shot learning. We compare the GREN network with the state-of-the-art algorithm in DHGD, and the results are shown in Table 3.  
For the different ways of learning, a mature scheme of one-shot learning combined with hand gesture recognition has not been proposed before. Those advanced methods of comparison adopt the way of recognizing large size samples for experiments. While our GREN network uses small size samples in the DHGD dataset and trains based on one-shot learning. Compared with other advanced algorithms, our method also performs well. For the 14-classes classification, the final accuracy of our GREN network is 82.29%, which is higher than most other algorithms. Additionally, our GREN network presents a higher accuracy in the 28-classes recognition than does that of the other advanced algorithm. A comparison of other advanced algorithms shows that the accuracy of the GREN network will not reduce significantly with the increase of the classes of hand gestures in the 28-classes recognition. Experimental results suggest that the proposed GREN network is an efficient method for hand gesture recognition. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & \multicolumn{2}{c}{**LSTM (\%)**} & **GREN (\%)** \\ \hline
14-classes & 1 & 75.18 & 78.65 \\
28-classes & 2 & 79.82 & 85.90 \\  & both & 76.89 & 82.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison results between long short-term memory (LSTM) and gesture recognition using an enhanced network (GREN) networks based on the DHGD dataset. Figure 6: The accuracy curve of our method for 14-classes and 28-classes in the DHGD dataset. Besides, to verify the robustness of the network, a similar experimental setup has also been performed on the MSRA hand gesture dataset. To more clearly demonstrate our network, we compared the experimental result with the LSTM network based on the MSRA dataset, which is shown in Table 4.  
From Table 4, the final accuracy of our network is 79.17% for the 17-classes classification. Additionally, compared with the LSTM networks, the accuracy of the recognition increased by approximately 6.25%, which shows the better performance of the GREN network.