# A Method of Ultrasonic Finger Gesture Recognition Based on the Micro-Doppler Effect
## 3 Results
### Discussion Parameters of the Recognition Model  
(1) _Number of Convolution layers_: To further explore the impact of the number of convolution layers on the recognition accuracy, recognition models under configurations of different numbers of convolution layers are tested. The number of convolution layers is set at 2, 3, 4 and 5, respectively, in each model, and the other parameters of the models are consistent with the initial settings. The recognition accuracies of different models are as shown in Table 2. It can be seen from the table that the recognition rate is the highest when the number of layers is three. However, when the number of layers increase to 4 and 5, the recognition rate falls because of over-fitting. The reason is that the increase of convolution layers leads to over parameterization of the models, and consequently making it difficult to be well trained by relatively number of samples. So, the number of convolution layer is fixed at 3. \begin{table}
\begin{tabular}{c|c c c c} \hline \hline  & \(3\times 3\times 64\) & \(3\times 3\times 64\) & \(3\times 3\times 64\) & \(3\times 3\times 64\) \\  & \(3\times 3\times 128\) & \(3\times 3\times 128\) & \(3\times 3\times 128\) & \(3\times 3\times 128\) \\ The Number of Convolution Layers & & \(3\times 3\times 256\) & \(3\times 3\times 256\) & \(3\times 3\times 256\) \\  & & & \(3\times 3\times 512\) & \(3\times 3\times 512\) \\  & & & & \(3\times 3\times 1024\) \\ \hline Recognition accuracies & 87.33\% & 95.48\% & 93.70\% & 92.67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recognition accuracies vs. Number of convolution layers. Figure 9: Illustrations of five finger gestures: (**a**) finger close, (**b**) finger clockwise circle, (**c**) finger slide, (**d**) finger open, (**e**) finger counterclockwise circle. (2) _Size of the Convolution Kernel_: In this experiment, the performance of recognition model is tested under different sizes of the convolution kernel. The sizes of the convolution kernel in all convolution layers are set as 3 \(\times\) 3, 4 \(\times\) 4 and 5 \(\times\) 5, respectively. Table 3 shows the recognition accuracies of different convolution kernel sizes. In Table 3, it can found that the recognition accuracy increases with the size of the kernel, and the number of parameters needed to be trained also increases. Therefore, the kernel with size of 4 \(\times\) 4 is selected. (3) _Number of Convolution Kernels_: The more the number of convolution kernels is, the more features are extracted from the convolution layer, and the easier it is to overfit. On the contrary, the fewer the number of convolution kernels is, the fewer the features extracted from the convolution layer are, and the more prone to under-fitting. The experiment's aim is to find the suitable number of convolution kernels. The Table 4 illustrates recognition accuracies of four different configurations. The result of Table 4 shows that the first configuration has a comparable accuracy, but it has minimum training parameters and minimum training cost. (4) _Number of FC layers_: In order to optimize architecture of the recognition model, the impact of number of the FC layers is examined. Four different configurations of FC layers are tested. Table 5 shows the results under different numbers of FC layers. From the table, it can be concluded that one FC layer is enough. (5) _Size of the FC layer_: The impact of the size of the FC layer is also examined by our experiments. Table 6 shows the recognition accuracies with different FC layer sizes vary from 128 to 1024. From the table, it can be known that the performance enhances the increase of the FC layer size. (6) _Number of Epochs_: The number of epochs is not enough and the model is not trained to be optimal. Too many epochs will require a long training time. Based on the chosen parameters in previous sections, the recognition model is trained with different numbers of epochs of 300, 500, 800