Abstract
To improve the performance of hand gesture recognition based on Kinect in human computer interaction system, a static hand gesture recognition framework integrated with depth data is put forward. The scheme makes full use of depth data to assist hand separation and acquires synchronized color and depth images by Kinect. The image contents are analyzed and extracted to track the hand area, with detailed feature descriptions. Finally, KNN is adopted as the classifier for training and recognition for static hand gesture, to avoid the problems in sample imbalance. The method proposed in this paper is tested in experiments with common static gestures. The conclusions are drawn in recognition rate, light and rotation, translation and scale change, to verify the feasibility and robustness of the scheme.
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