A Human-Machine Interaction Technique: Hand Gesture Recognition Based on Hidden Markov Models with Trajectory of Hand Motion

We have developed an efficient mechanism for real-time hand gesture recognition based on the trajectory of hand motion and the hidden Markov models classifier. In our system, we divide our gestures into single or both hands, one hand have been defined four basic types of directive gesture such as moving upward, downward, leftward, rightward. Then, two hands have twenty-four kinds of combination gesture. However, we apply the most natural and simple way to define eight kinds gestures in our developed human-machine interaction control system so that the users can easily operate the robot. Experimental results reveal that the face tracking rate is more than 97% in general situations and over 94% when the face suffers from temporal occlusion. The efficiency of system execution is very satisfactory, and we are encouraged to commercialize the robot in the near future.