Figure 3. Framework of the classification scheme.

In (A), 63 features are extracted for each image in the dataset using the MediaPipe Hands pipeline. Then, data is split with stratification into 90–10 train/validation-test sets as shown in (B). Features are then scaled using Standard Scaler. In (C), hyperparameter tuning is employed to ensure best performance for each model. Using the hyperparameter combination that resulted with the best accuracy, the models are trained on the train/validation set and evaluated on the test set using five performance metrics. Comparison is implemented from this result.