## 4 Results
### Sign Recognition Accuracies: Ensemble Methods

Table 10: Ensemble accuracies for different numbers of PCA components in convolutional-related features, and hand coordinates. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multirow{2}{*}{\begin{tabular}{c} **ASL** \\ **Alphabet** \\ \end{tabular} } & \begin{tabular}{c} **ASL** \\ **Finger-** \\ **Spelling** \\ **Dataset** \\ \end{tabular} & \begin{tabular}{c} **Cambridge** \\ **Hand** \\ **Dataset** \\ \end{tabular} & \multirow{2}{*}{\
\begin{tabular}{c} **NUS I** \\ **Dataset** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **NUS II** \\ **Dagits** \\ \end{tabular} } \\  & & **Dataset** & & & \\ \hline
**Convolutional features** & 10 & 10 & 10 & 10 & 10 & 5 \\
**Convolutional features + finger angles** & 10 & 10 & 10 & 10 & 10 & 5 \\
**CNN features on hand edges** & 15 & 5 & 15 & 10 & 20 & 5 \\
**CNN features on BRISK** & 15 & 15 & 15 & 10 & 15 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Ideal number of PCA components. only exception is the Cambridge hand gesture dataset because it comprises sequential data which means that the coordinates change within the same class. Secondly, for convolutional-related features, XGBoost performs less than or equal to random forest. This is because random forest uses the bagging technique to train multiple subsets of the dataset on various decision trees to improve overall accuracy, whereas XGBoost uses the boosting technique to iteratively improve prediction for misclassified instances. However, since PCA is performed on the convolutional-related features, there seems to be a loss of information for XGBoost to make any further improvement. This does not seem to be true for the NUS I and II datasets using 'CNN features on hand edges' and the NUS II dataset using 'Convolutional features' for XGBoost, where the number of principal components seem to be sufficient enough to rectify itself on the misclassified sign gestures and consequently surpass random forest. Moving on to the next inference, 'CNN features on hand edges' gives better accuracy compared to 'Convolutional features' alone since the system has segmented the hand region and retained only the outline in the form of edges. This has made the resultant image free from any background complexity and hence easier to learn by the ensemble models. However, it does have the following exceptions--the ASL finger-spelling dataset and the NUS II dataset (due to retention of background edges within the range of the skin mask thus adding background complexity), and the ASL alphabet dataset (due to deletion of hand edges falling outside of the skin mask which gives an incorrect hand outline not generalized by the ensemble models). It can also be observed that the NUS I and II datasets give the lowest accuracies when tested with the proposed features on the ensemble models. This is because the classes in the NUS I dataset appear to be similar, and there is a variety of different backgrounds even within the same class for the NUS II dataset. These factors add complex non-linear patterns in the data which the ensemble models are unable to learn. On the other hand, the Cambridge hand gesture dataset records the highest accuracies among all the datasets due to the simplistic background of the input images, uniform and distinguishable hand gestures for each sign and the hand region in all the images falling within the skin-mask. It can be noted from Table 10 that the NUS I and II datasets give the highest accuracies with hand coordinates due to their perfect pinpointing of the hand region despite complicated backgrounds or minute differences between sign image classes. Moreover, convolutional features give better accuracy than CNN features on BRISK. This is because the latter extracts features from keypoints computed over an approximate hand outline due to the application of an approximate skin mask. Due to the absence of parts of the hand region over the various datasets and thus an absence of keypoints, there is some information loss for CNN features on BRISK and hence lesser accuracy.