Gesture Recognition Based on Depth Information and Convolutional Neural Network
Du Jiang, Gongfa Li, Guozhang Jiang, Disi Chen, Zhaojie Ju · 2018
Vision-based gesture recognition accords with natural communication habits of human and can carry out long-distance and non-contact interactions. So it has become a hot direction in human-computer interaction research whose recognition effect largely depends on the performance of image preprocessing and recognition algorithms. In this paper, a gesture recognition method using color image and depth image combined is designed. For the influence of the angle on the same gesture, the skeleton algorithm is optimized based on the layer-by-layer stripping concept. The fast refinement algorithm improves the process of repeated scanning, extracts the key node information in the skeleton map of the hand, and establishes the spatial axis of the hand to determine the gesture direction. The gesture recognition experiment was performed based on convolutional neural network. The results showed the recognition accuracy rate was 96.01%, and the robustness and accuracy of the proposed recognition method were verified.