{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:03:18Z","timestamp":1753884198749,"version":"3.41.2"},"reference-count":25,"publisher":"World Scientific Pub Co Pte Ltd","issue":"15","funder":[{"name":"Tianjin science and technology project","award":["21YDTPJC00050"],"award-info":[{"award-number":["21YDTPJC00050"]}]},{"name":"science and technology on electro-optical information security control laboratury project","award":["2021JCJQLB055008"],"award-info":[{"award-number":["2021JCJQLB055008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2022,10]]},"abstract":"<jats:p> The era of new human\u2013computer interaction has accelerated, and gesture recognition is one of the development trends of human\u2013computer interaction system in the future. The emerging graph neural network can capture the interdependence between instances and infer the complete information of the image based on local features, which is helpful to the recognition of human gestures. Therefore, the combination of graph neural network and convolutional neural network (CNN) is applied to the research of gesture recognition and it has important research significance. This paper uses the Mixup method to perform data enhancement processing on the collected image data set, applies the pyramid pooling method to the EigenPooL model to achieve efficient capture of image features and selects the Sigmoid function and the PReLU function as the activation function of the network model to meet the model\u2019s requirements, such as long time training and strong fitting ability. This paper introduces the structure and algorithm of EigenPooL model in detail. The algorithm uses hypergraph learning method instead of simple graph learning method. On the gesture picture test set, the average accuracy of the algorithm is 86.50%, the recall rate is 94.87% and the average detection time per frame is 421[Formula: see text]ms. <\/jats:p>","DOI":"10.1142\/s0218126622502711","type":"journal-article","created":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T02:37:37Z","timestamp":1653273457000},"source":"Crossref","is-referenced-by-count":1,"title":["Graph Convolutional Neural Network Gesture Recognition Based on Pooling Algorithm"],"prefix":"10.1142","volume":"31","author":[{"given":"Hong","family":"Chen","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300130, P. R. China"},{"name":"School of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Qinhuangdao 066004, Hebei, P. R. China"}]},{"given":"Baoqiang","family":"Qi","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Qinhuangdao Institute of Technology, Qinhuangdao 066100, Hebei, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1251-0943","authenticated-orcid":false,"given":"Hongdong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300130, P. R. China"},{"name":"Science and Technology on Electro-Optical Information, Security Control Laboratory, Tianjin 300308, P. R. 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