  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.
