# Recursive Feature Elimination for Improving Learning Points on Hand-Sign Recognition
## 4 Results and Discussion
### Discussion
#### 4.2.3 Advantages of Our Method Compared to Using CNN  
In our proposed methods, we chose the combination of Mediapipe for feature extraction and neural networks for classification instead of using CNN, which can conduct feature extraction and classification by itself. As seen in Figure 12, a simple CNN with three convolutional layers, three batch normalization layers, three max-pooling layers, and three dense layers has more than 5 million parameters, while in our proposed method, we used a simple neural network with three dense layers only, as shown in Figure 13.  
Figure 12: Example of a simple CNN architecture.  
Table 4 shows the comparison between CNN and the classification model used in our proposed method when trained using Dataset 1. As we can see, CNN has more parameters, which results in longer training time. This could be attributed to the feature extraction ability of CNN, which causes the number of parameters to be much larger. In our proposed method, the training time recorded is purely the time to train the model. The feature extraction step is performed during the data preprocessing. For Dataset 1, which has more than 30,000 images after augmentation, the data preprocessing took 2885 s. Though the data preprocessing took a long time to finish, it is a one-time process for each dataset, as there is no need to conduct preprocessing every time we want to train our model. The data preprocessing is not only for feature extraction, it also cleans the data by removing images that cannot be used for training, labeling, one-hot encoding for the classes, and also saving the result into a.npy file to be used in the future processes. By separating the feature extraction process from the actual training process, our approach benefits from faster model training time. This will be very beneficial when conducting experiments by adjusting training hyperparameters and other configurations. We can perform more experiments with shorter training time and adjust the hyperparameters and model configuration to obtain the best model.  
As seen in Table 4, trained using the same dataset, CNN shows good accuracy on the train-test data with 100% accuracy in testing. However, it only achieves 57.7% accuracy when tested on Dataset 4, while our proposed method can achieve 96.3% accuracy. This shows that in training a CNN, more images with many variations and different backgrounds are needed before it can generalize well. Suppose the model is only trained using images  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CNN** & \multicolumn{2}{c}{**Proposed Approach**} \\ \hline Input & 100 \(\times\) 100 pixels image & Ten hand landmarks extracted using Mediapipe \\ \hline Epoch & 30 & 30 & 100 \\ \hline Batch size & 128 & 512 & 512 \\ \hline Training accuracy & 92.79\% & 94.3\% & **98.3\%** \\ \hline Testing accuracy & 100\% & 90.8\% & **93.40\%** \\ \hline Training time & 1607 s & 62.1 s * & 198.5 s * \\ \hline Validation accuracy & 57.7\% & 95.3\% & **96.3\%** \\ \hline Evaluation time & 2.4 s & **1 s** & **1 s** \\ \hline \hline \end{tabular}  
* does not include the time needed for feature extraction.  
\end{table}
Table 4: Comparison between CNN and our proposed approach.  
Figure 13: The classification model in our proposed method.  
with light-colored and monotone backgrounds. In that case, it will not perform well in a complex background, which is more likely to happen in real-life situations. At the same time, most publicly available hand-sign datasets are taken in uniform settings with no complex background. By using Mediapipe for hand detection and feature extraction, our classification model can focus on learning the pattern of the hand landmarks for ASL digit hand-sign classification and achieve far better accuracy.