Abstract
The Hand Gestures Recognition (HGR) System can be employed to facilitate communication between humans and computers instead of using special input and output devices. These devices may complicate communication with computers especially for people with disabilities. Hand gestures can be defined as a natural human-to-human communication method, which also can be used in human-computer interaction. Many researchers developed various techniques and methods that aimed to understand and recognize specific hand gestures by employing one or two machine learning algorithms with a reasonable accuracy. This work aims to develop a powerful hand gesture recognition model with a 100% recognition rate. We proposed an ensemble classification model that combines the most powerful machine learning classifiers to obtain diversity and improve accuracy. The majority voting method was used to aggregate accuracies produced by each classifier and get the final classification result. Our model was trained using a self-constructed dataset containing 1600 images of ten different hand gestures. The employing of canny's edge detector and histogram of oriented gradient method was a great combination with the ensemble classifier and the recognition rate. The experimental results had shown the robustness of our proposed model. Logistic Regression and Support Vector Machine have achieved 100% accuracy. The developed model was validated using two public datasets, and the findings have proved that our model outperformed other compared studies.