# Skeleton-Based Dynamic Hand Gesture Recognition Using an Enhanced Network with One-Shot Learning
## 6 Conclusions  
This paper proposes the GREN network to recognize dynamic hand gestures based on a small number of skeleton-based sequence samples. According to the MANN network, the ability to store and update sequence data is further enhanced by introducing the average pooling layer (avgpool) and batch normalization (BN), so that we can combine the hand skeleton sequence with the GREN network to achieve dynamic hand gesture recognition based on one-shot learning. Experiments with the DHGD hand gesture dataset demonstrate the state-of-the-art performance of the GREN network for skeleton-based dynamic hand gesture recognition based on one-shot learning. Additionally, the MSRA hand gesture dataset verifies the robustness of our GREN network.  
Conceptualization, Y.Q.; methodology, A.W.; software, S.Z.; supervision, G.C.; validation, Y.Q.; writing--original draft, S.Z.; writing--review and editing, C.M. All authors have read and agreed to the published version of the manuscript. This research was funded by Fundamental Research Funds for the Central Universities grant number 201762005, the National Natural Science Foundation of China grant number 41906155, and the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao) grant number 2019 GHZ023.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **LSTM (\%)** & **GREN (\%)** \\ \hline
17-classes & 72.92 & 79.17 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results between LSTM and GREN networks based on the MSRA dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Learning** & **Methods** & \
\begin{tabular}{c} **Accuracy 14-Classes** \\ **Gestures** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy 28-Classes** \\ **Gestures** \\ \end{tabular} \\ \hline \multirow{8}{*}{**Large-samples**} & HON4D: Histogram of Oriented 4D & \multirow{8}{*}{75.53\%} & \multirow{8}{*}{74.03\%} \\  & Normals for Activity Recognition from & & \\  & Depth Sequences [46] & & \\ \cline{1-1} \cline{3-5}  & 3-D Human Action Recognition by Shape & & \\  & Analysis Of Motion Trajectories on & 79.61\% & 62.00\% \\  & Riemannian Manifold [47] & & \\ \cline{1-1} \cline{3-5}  & Joint Angles Similarities and HOG2 for & \multirow{2}{*}{80.85\%} & \multirow{2}{*}{76.53\%} \\  & Action Recognition [48] & & \\ \cline{1-1} \cline{3-5}  & Key Frames with Convolutional Neural & & \\  & Network [18] & & \\ \cline{1-1} \cline{3-5}  & Skeleton-Based Dynamic Hand Gesture & \multirow{2}{*}{83.07\%} & \multirow{2}{*}{79.14\%} \\  & Recognition [49] & & \\ \cline{1-1} \cline{3-5}  & NUKF-LSTM [44] & & \\ \cline{1-1} \cline{3-5}  & SL-Fusion-Average [36] & & \\ \cline{1-1} \cline{3-5}  & MFA-Net [29] & & \\ \hline
**One-shot** & GREN & **82.29\%** & **82.03\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Result of different method comparison for 14/28-classes gestures on dynamic hand gesture dataset using skeleton-based data.  
**Acknowledgments:** The authors gratefully acknowledge the support of the Fundamental Research Funds for the Central Universities (Grant No.: 201762005), National Natural Science Foundation of China (Grant No.: 41906155), and Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology, Qingdao (Grant No.: 2019 GHZ023).  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.