Research on Dynamic and Static Fusion Polymorphic Gesture Recognition Algorithm for Interactive Teaching Interface

Abstract

In order to solve the problem of teachers’ excessive energy dissipation due to interaction with teaching equipment in traditional classrooms, an interactive and intelligent teaching interface is proposed to enable teachers to use the gestures to give students a geometry lesson. The traditional algorithm of gesture recognition mainly consists of feature extraction and classifier, which requires human-designed features. The recognition is mainly based on static gesture or dynamic gesture singular state recognition algorithm. The recognition accuracy is not robust enough and different people Identification results do not have the universality and ease of operation. In order to solve this problem, we propose a multi-state gesture recognition algorithm based on the deep learning network, which combines the large database of hand gestures and the deep learning algorithms. The innovation of this algorithm is as follows: Aiming at the static gesture images, a sequence reduction algorithm is proposed. According to the sequence of dynamic gestures, the first and last frame fixed and intermediate frame traversal combination algorithm are proposed to get the dynamic and static fusion gesture training datasets, and then the dynamic and static fusion datasets are input to the deep learning network GoogLeNet for training. After repeated training, we found the optimal rule of deep learning network training. According to the optimization law, we got GoogLeNet_model which can recognize 23 kinds of dynamic and static fusion gestures, the recognition rate is 97.09%. We use this model in interactive teaching interface, and achieved good application effect.