Hand gesture is one of the most popular communication methods in everyday life. In human-computer interaction applications, hand gesture recognition provides a natural way of communication between humans and computers. There are mainly two methods of hand gesture recognition: glove-based method and vision-based method. In this paper, we propose a vision-based hand gesture recognition method using Kinect. By using the depth information is efficient and robust to achieve the hand detection process. The finger labeling makes the system achieve pose classification according to the finger name and the relationship between each fingers. It also make the classification more effective and accutate. Two kinds of gesture sets can be recognized by our system. According to the experiment, the average accuracy of American Sign Language(ASL) number gesture set is 94.33%, and that of general gestures set is 95.01%. Since our system runs in real-time and has a high recognition rate, we can embed it into various applications.

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This paper presents a gesture recognition interface with the observed pose sequence determined by our upper body model-based pose tracking. For last decade many researchers have focused on how well tracks human poses based on predefined pose model. Then we move this discussion to the gesture recognition by pose tracking. Our system consists of two parts: pose tracking and gesture recognition. In the first part, Particle filtering is used for tracking the upper body pose with the key pose library where we try to find key pose for the proposal distribution. The particles generated from the proposal distribution with random noise, could cover the pose space which is not covered by the key pose library. In second part, the observed pose is labeled with a pose number among all key poses by comparing between key poses. HMM is used to determine the probabilities of the gesture states from the observed pose sequence. HMM parameters like transition and emission matrix are trained by the analysis on the gesture database. The experimental results shows how well gesture recognition works based on our system.

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