Recognition of Hand Gestures Using Image with Histogram Feature Extraction and Euclidean Distance Classification Method

Human-Computer Interaction technology allows humans to communicate with computers using natural language such as gestures. One of the gestures used by humans to communicate is hand gestures. In this research, hand gestures are recognized using images that represent pitch, roll, and yaw movement. As a feature extraction method, the histogram is used, while the classification method used is the Euclidean distance. From the experimental results, the combination of the histogram feature extraction method and the Euclidean distance classification method had an accuracy of 30 percent for images representing pitch and yaw movements, and 60 percent for images representing roll movements.