# Dynamic Gesture Recognition Based on MEMP Network
## 4 Experiment
### Experimental Results  
**LSA**: 2D CNN, 3D CNN, 3D CNN + LSTM and the MEMP network structure were respectively used to train LSA data set. The network structure proposed in the paper [11] is 2D CNN + LSTM. Therefore, there are four groups of experiments in this data set, and the experimental results are shown in Table 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment Dataset** & **LSA** & \\ \cline{2-3}
**Records** & **Method** & **Accuracy** \\ \hline
1 & 2D CNN & 93.563\% \\
2 & 2D CNN + LSTM [11] & 95.217\% \\
3 & 3D CNN & 97.892\% \\
4 & 3D CNN + LSTM & 98.451\% \\
5 & Our Method (MEMP network) & 99.063\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: LSA experimental results. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Gesture Video** & **RGB Part** & **Depth Part** \\ \hline Training & 35,878 & 35,878 & 35,878 \\ Validation & 5784 & 5784 & 5784 \\ Testing & 6271 & 6271 & 6271 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical information of IsoGD dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Gesture Video** & **RGB Part** & **Depth Part** \\ \hline All & 1080 & 1080 & 1080 \\ Training & 80\% randomly & 80\% randomly & 80\% randomly \\ Testing & 20\% randomly & 20\% randomly & 20\% randomly \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical information of SKIG dataset. As can be seen from Table 4, the MEMP neural network structure proposed in this paper has improved the recognition rate of LSA data sets by 3.846% compared with the structure proposed in this paper [11], and the MEMP network is 1.171% higher than the 3D CNN network frequently used in video processing. It can be seen that this network structure has high accuracy in processing LSA data sets. Figure 3 show the accuracy and loss function change of LSA data set during the training of this network. The epoch of the entire network is 100. The loss function used is 'categorical-crossentropy'. The yellow line represents the verification set and the blue line represents the training set. From this we can see that when the epoch reaches 40, the accuracy and loss function of the network tends to be stable. This shows the stability of the network. **IsoGD**: in this data set, the MEMP network and the 3D CNN network are used to train RGB and rgb-depth data sets respectively. In the paper [20], the author used C3D and LSTM networks to train IsoGD data. The experimental results are shown in Table 5.  
It can be seen from the results that, in the IsoGD data set, the accuracy of MEMP network is 10.43% higher than that of the paper [20], which is a big breakthrough. In the same data set, MEMP network was 24.89% higher in RGB and 23.73% higher in rgb-depth data sets than the commonly used 3D CNN network. In comparison with the paper [3], we have improved 30.69% and 27.83% in RGB and RGB-Depth part respectively. These improvements illustrate the importance of multi-fetch multi-prediction for frame sets. In the experiment, the size of frame is \(64\times 64\), which is smaller  
Figure 3: Accuracy on LSA dataset using MEMP network. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment Dataset** & **IsoGD** & \\ \cline{2-3}
**Records** & **Method** & **Accuracy** \\ \hline
1 & 2D CNN (RGB) & 46.03\% \\
2 & 2D CNN (RGB-Dep) & 49.17\% \\
3 & 3D CNN (RGB) & 49.68\% \\
4 & 3D CNN (RGB-Dep) & 55.12\% \\
5 & 3D CNN and ConvLSTM (RGB) [3] & 43.88\% \\
6 & 3D CNN and ConvLSTM (RGB-Dep) [3] & 51.02\% \\
7 & Res-C3D and Skeleton LSTM (RGB-Dep) [20] & 68.42\% \\
8 & ResNet-18 (RGB-Dep) & 66.18\% \\
9 & ResNet-34 (RGB-Dep) & 67.54\% \\
10 & 3 DResNet-18 (RGB-Dep) & 71.24\% \\
11 & MEMP network (RGB) & 74.57\% \\
12 & MEMP network (RGB-Dep) & 78.85\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: IsoGD experimental results. than the size of paper [3] and paper [20]. This shows that MEMP network can save a lot of time. Compared with the current popular ResNet and 3D ResNet, the MEMP network also has a significant improvement in accuracy.