Integrating Color and Depth Cues for Static Hand Gesture Recognition

Recognizing static hand gesture in complex backgrounds is a challenging task. This paper presents a static hand gesture recognition system using both color and depth information. Firstly, the hand region is extracted from complex background based on depth segmentation and skin-color model. The Moore-Neighbor tracing algorithm is then used to obtain hand gesture contour. The k-curvature method is used to locate fingertips and determine the number of fingers, then the angle between fingers are generated as features. The appearance-based features are integrated to the decision tree model for hand gesture recognition. Experiments have been conducted on two gesture recognition datasets. Experimental results show that the proposed method achieves a high recognition accuracy and strong robustness.