Gesture recognition matching based on dynamic skeleton
Jingyao Wang, Naigong Yu, Firdaous Essaf · 2021
Gestures, as a basic human feature, occupy an important position in human-computer interaction and other fields as well. In order to accurately recognize gestures and eliminate environmental interference, this paper proposes a gesture recognition matching method based on dynamic bones. This method uses the Mask R-CNN model and exponential filtering to identify and calibrate the key points of the hand. Through the segmentation and feature extraction of real-time frame images, the combined network is used to obtain stable and accurate hand bone key points. Based on the idea of constructing a Spatio-temporal graph convolutional network model based on ST-GCN, a skeletal point information gesture data set is constructed and sent to the network for training. Finally, template matching is used to realize gesture recognition. The experimental results show that the method can eliminate the environmental interference to the greatest extent, as well as the incomplete traditional data set, and the model accuracy defects caused by the lack of special samples. The recognition accuracy of the Chinese sign language database can reach 87.02%. Compared with previous researches on gesture recognition, it has improved accuracy and robustness.