RealTime 3D Hand Gesture Based Mobile Interaction Interface Hand gesture recognition is a challenging problem for natural humancomputer interaction(HCI). We address this problem by introducing a realtime humanmobile interaction interface with a depth sensor. Our interface consists of two components, 3D hand pose estimation and hand skeleton state based gesture description. Firstly, we propose a 3D hand pose estimation method that combines learning based pose initialization and physical based model fitting, which can estimate the perframeu0027s hand pose that appears in the depth camerau0027s field of view. Afterwards, we map the estimated pose to gesture, e.g. open or close, through a hand skeleton state based method. With the tracked hand gesture, we can stably and smoothly implement common operations such as u0027Touchu0027, u0027Graspu0027 and u0027Holdu0027 with midair interface. Our main contribution is combine 3D hand pose estimation and hand gesture tracking, and implementing an interaction application system with the details.

Depth based Hand Gesture Recognition for Smart Teaching Gesture recognition plays a very important role in humancomputer 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 humancomputer 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.