# Hand Gesture Recognition via Lightweight VGG16 and Ensemble Classifier
## 4 Experiments
### Pre-Work in Feature Extractor and Classifier Selection
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Conv Block**} & \multirow{2}{*}{**Total Parameter**} & \multirow{2}{*}{**Feature Length**} & \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Accuracy (\%) (Execution Time)**} \\ \cline{5-6}  & & & & **Random Forest** & **XGBoost** & **LightGBM** \\ \hline \multirow{2}{*}{5 conv blocks} & \multirow{2}{*}{14,716,736} & \multirow{2}{*}{512} & D1 & 99.74 (75.1 s) & 99.53 (1365.9 s) & 99.74 (133.3 s) \\  & & & D2 & 100 (4.6 s) & 100 (54.1 s) & 100 (24.2 s) \\  & & & D3 & 100 (3.5 s) & 100 (35.5 s) & 100 (14.3 s) \\ \hline \multirow{2}{*}{**4 conv blocks**} & \multirow{2}{*}{**7,637,312**} & \multirow{2}{*}{**4608**} & **D1** & **99.98 (330 s)** & **99.98 (9968 s)** & **99.99 (2789 s)** \\  & & & **D2** & **100 (17.6 s)** & **100 (328.4 s)** & **100 (534.7 s)** \\  & & & **D3** & **100 (12.8 s)** & **100 (220.9 s)** & **100 (2419 s)** \\ \hline \multirow{2}{*}{3 conv blocks} & \multirow{2}{*}{1,736,512} & \multirow{2}{*}{9216} & D1 & 99.99 (603.5 s) & 99.95 (25,938 s) & 100 (5375 s) \\  & & & D2 & 100 (29.8 s) & 100 (637.5 s) & 100 (1099.7 s) \\  & & & D3 & 100 (20.2 s) & 100 (415.2 s) & 100 (509.6 s) \\ \hline \multirow{2}{*}{2 conv blocks} & \multirow{2}{*}{268,672} & \multirow{2}{*}{18,432} & D1 & \multicolumn{2}{c}{Unable to run due to out of memory} \\  & & & D2 & 100 (53.9 s) & 100 (1544 s) & 100 (2145 s) \\ \cline{1-1}  & & & D3 & 100 (38.4 s) & 100 (1078 s) & 100 (933 s) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the pre-work for classifier selection. \begin{table}
\begin{tabular}{l l c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Epoch**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{1}{c}{**Average**} \\  & & **Completed** & & **Accuracy** \\ \hline \multirow{4}{*}{MobileNetV2} & D1 & 91 & 99.95\% & \multirow{4}{*}{99.93\%} \\  & D2 & 58 & 99.84\% & & \\  & D3 & 88 & 100.00\% & & \\ \hline \multirow{2}{*}{**VGG16**} & **D1** & **22** & **100.00\%** & \\  & **D2** & **20** & **99.92\%** & **99.97\%** \\  & **D3** & **23** & **100.00\%** & \\ \hline \multirow{2}{*}{ResNet50V2} & D1 & 100 & 99.82\% & \multirow{4}{*}{99.87\%} \\  & D2 & 61 & 100.00\% & & \\  & D3 & 83 & 99.80\% & \\ \hline \multirow{2}{*}{InceptionV3} & D1 & 100 & 96.84\% & \multirow{4}{*}{84.94\%} \\  & D2 & 100 & 98.89\% & & \\  & D3 & 100 & 59.10\% & & \\ \hline \multirow{2}{*}{InceptionResNetV2} & D1 & 59 & 99.95\% & \multirow{4}{*}{99.91\%} \\  & D2 & 40 & 99.76\% & & \\  & D3 & 65 & 100.00\% & \\ \hline \multirow{2}{*}{DenseNet169} & D1 & 100 & 99.20\% & \multirow{4}{*}{84.99\%} \\  & D2 & 100 & 95.39\% & & \\  & D3 & 100 & 60.40\% & \\ \hline \multirow{2}{*}{Xception} & D1 & 92 & 99.92\% & \multirow{4}{*}{99.97\%} \\  & D2 & 82 & 100.00\% & & \\  & D3 & 100 & 100.00\% & \\ \hline \multirow{2}{*}{NASNetMobile} & D1 & 100 & 92.37\% & \multirow{4}{*}{73.38\%} \\  & D2 & 100 & 88.08\% & & \\  & D3 & 100 & 39.70\% & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the pre-work for feature extractor selection.