In the field of human-computer interaction, vision-based gesture recognition methods are widely studied. However, its recognition effect depends to a large extent on the performance of the recognition algorithm. The skeletonization algorithm and convolutional neural network (CNN) for the recognition algorithm reduce the impact of shooting angle and environment on recognition effect, and improve the accuracy of gesture recognition in complex environments. According to the influence of the shooting angle on the same gesture recognition, the skeletonization algorithm is optimized based on the layer-by-layer stripping concept, so that the key node information in the hand skeleton diagram is extracted. The gesture direction is determined by the spatial coordinate axis of the hand. Based on this, gesture segmentation is implemented to overcome the influence of the environment on the recognition effect. In order to further improve the accuracy of gesture recognition, the ASK gesture database is used to train the convolutional neural network model. The experimental results show that compared with SVM method, dictionary learning + sparse representation, CNN method and other methods, the recognition rate reaches 96.01%.
Layer-by-layer stripping theory Skeletonization algorithm Convolutional neural network Gesture recognition Big data
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This work was supported by grants of National Natural Science Foundation of China (Grant No. 51575407, 51575338, 51575412, 61273106, 51505349) and the Grants of National Defense Pre-Research Foundation of Wuhan University of Science and Technology (GF201705). This paper is funded by Wuhan University of Science and Technology graduate students short-term study abroad special funds.
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