Control of computer pointer using hand gesture recognition in motion pictures

A user interface is designed to control the computer cursor by hand detection and classification of its gesture. A hand dataset with 6720 image samples is collected, including four classes: fist, palm, pointing to the left, and pointing to the right. The images are captured from 15 persons in simple backgrounds and different perspectives and light conditions. A CNN network is trained on this dataset to predict a label for each captured image and measure the similarity of them. Finally, commands are defined to click, right-click and move the cursor. The algorithm has 91.88% accuracy and can be used in different backgrounds.