Depth based Hand Gesture Recognition for Smart Teaching

Gesture recognition plays a very important role in human-computer interaction, and depth based gesture recognition receives more attention because depth sensors have the advantages of capturing depth information and being robust to illumination changes. At present, gesture recognition algorithms focus on the accuracy and efficiency of recognition on general data sets, but ignore the specific needs of interactive gestures in specific scenarios, and the general gesture data sets can not meet the actual interactive needs, which also limits the application and promotion of human-computer interaction. Aiming at the above problems, this paper creates a specific hand gesture data set, which dedicated to interactive teaching of intelligent classroom teaching, and proposes a deep neural network model which integrates global and local information for gesture recognition. The experimental results demonstrate that the proposed deep model achieves 93.6% recognition rate of 17 commonly used gestures and verifies the performance in virtual geometry teaching.