Static Hand Gesture Recognition Method Based on Depth Images with Shape Context Feature

Static hand gesture recognition is very important for human-computer interaction systems, which is widely used in human-computer interaction systems. Using visible RGB images as the input method would be more stable, but the illumination and skin color are big problems; by contrast the depth images is a better choice. A novel algorithm which recognizes static hand gesture based on depth images using 3d shape context feature and improves the performance by arm major axis correction and contour-center sampling is proposed. Arm major axis correction can solve the rotation problem and the sampling, which increases the recognition rate. Besides, using 3d space information makes the algorithm more stable. Experimental results show that the average recognition rate gets to 95.8%, and the performance and speed are superior to existing algorithms. Recognition results can be widely used in the follow-on real scene human-computer interaction (HCI) operations.