{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:18:14Z","timestamp":1775578694903,"version":"3.50.1"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T00:00:00Z","timestamp":1568332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T00:00:00Z","timestamp":1568332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Image Video Proc."],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Hand gesture recognition methods play an important role in human-computer interaction. Among these methods are skeleton-based recognition techniques that seem to be promising. In literature, several methods have been proposed to recognize hand gestures with skeletons. One problem with these methods is that they consider little the connectivity between the joints of a skeleton, constructing simple graphs for skeleton connectivity. Observing this, we built a new model of hand skeletons by adding three types of edges in the graph to finely describe the linkage action of joints. Then, an end-to-end deep neural network, hand gesture graph convolutional network, is presented in which the convolution is conducted only on linked skeleton joints. Since the training dataset is relatively small, this work proposes expanding the coordinate dimensionality so as to let models learn more semantic features. Furthermore, relative coordinates are employed to help hand gesture graph convolutional network learn the feature representation independent of the random starting positions of actions. The proposed method is validated on two challenging datasets, and the experimental results show that it outperforms the state-of-the-art methods. Furthermore, it is relatively lightweight in practice for hand skeleton-based gesture recognition.<\/jats:p>","DOI":"10.1186\/s13640-019-0476-x","type":"journal-article","created":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T04:39:46Z","timestamp":1568349586000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":91,"title":["Spatial temporal graph convolutional networks for skeleton-based dynamic hand gesture recognition"],"prefix":"10.1186","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9685-2571","authenticated-orcid":false,"given":"Yong","family":"Li","sequence":"first","affiliation":[]},{"given":"Zihang","family":"He","sequence":"additional","affiliation":[]},{"given":"Xiang","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Zuguo","family":"He","sequence":"additional","affiliation":[]},{"given":"Kangrong","family":"Han","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,13]]},"reference":[{"key":"476_CR1","doi-asserted-by":"crossref","unstructured":"D. Tang, J. Taylor, P. Kohli, C. Keskin, T. K. Kim, J. Shotton, Opening the black box: hierarchical sampling optimization for estimating human hand pose. Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 3325\u20133333 (2015).","DOI":"10.1109\/ICCV.2015.380"},{"key":"476_CR2","doi-asserted-by":"crossref","unstructured":"Q. Ye, S. Yuan, T. K. Kim, Spatial attention deep net with partial pso for hierarchical hybrid hand pose estimation. Eur. Conference on Computer Vision (ECCV), 346\u2013261 (2016).","DOI":"10.1007\/978-3-319-46484-8_21"},{"key":"476_CR3","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1016\/j.jvcir.2018.04.005","volume":"55","author":"G. Wang","year":"2018","unstructured":"G. Wang, X. Chen, H. Guo, C. Zhang, Region ensemble network: towards good practices for deep 3d hand pose estimation. J. Vis. Commun. Image Represent.55:, 404\u2013414 (2018).","journal-title":"J. Vis. Commun. Image Represent."},{"key":"476_CR4","doi-asserted-by":"crossref","unstructured":"P. Molchanov, X. Yang, S. Gupta, K. Kim, S. Tyree, J. Kautz, Online detection and classification of dynamic hand gestures with recurrent 3d convolutional neural network. Comput. Vis. Pattern Recog. (CVPR), 4207\u20134215 (2016).","DOI":"10.1109\/CVPR.2016.456"},{"key":"476_CR5","doi-asserted-by":"publisher","first-page":"1692","DOI":"10.1109\/TPAMI.2015.2461544","volume":"38","author":"N. Neverova","year":"2014","unstructured":"N. Neverova, C. Wolf, G. Taylor, F. Nebout, Moddrop: Adaptive multi-modal gesture recognition. IEEE Trans. Pattern. Anal. Mach. Intell.38:, 1692\u20131706 (2014).","journal-title":"IEEE Trans. Pattern. Anal. Mach. Intell."},{"key":"476_CR6","doi-asserted-by":"crossref","unstructured":"Q. D. Smedt, H. Wannous, J. P. Vandeborre, 3d hand gesture recognition by analysing set-of-joints trajectories. Eurographics Work. 3D Object Retr., 86\u201397 (2017).","DOI":"10.1007\/978-3-319-91863-1_7"},{"key":"476_CR7","doi-asserted-by":"crossref","unstructured":"X. Yang, Y. Tian, Eigenjoints-based action recognition using naive-bayes-nearest-neighbor. Proc. IEEE Conf. Comput. Vis. Pattern Recog. Workshops, 14\u201319 (2012).","DOI":"10.1109\/CVPRW.2012.6239232"},{"key":"476_CR8","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.patcog.2016.01.020","volume":"55","author":"H. Chen","year":"2016","unstructured":"H. Chen, G. Wang, J. Xue, A novel hierarchical framework for human action recognition. Pattern Recognit.55:, 148\u2013159 (2016).","journal-title":"Pattern Recognit."},{"key":"476_CR9","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.patcog.2017.10.033","volume":"76","author":"J. C. Nunez","year":"2018","unstructured":"J. C. Nunez, R. Cabido, J. J. Pantrigo, A. S. Montemyaor, J. F. Velez, Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition. Pattern Recognit.76:, 80\u201396 (2018).","journal-title":"Pattern Recognit."},{"key":"476_CR10","doi-asserted-by":"crossref","unstructured":"A. Graves, Long Short-Term Memory. Supervised Sequence Labelling Recurrent Neural Netw., 37\u201345 (2012). Springer Berlin Heidelberg.","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"476_CR11","unstructured":"X. Chen, H. Guo, G. Wang, L. Zhang. Motion feature augmented recurrent neural network for skeleton-based dynamic hand gesture recognition, (2017), pp. 2881\u20132885."},{"key":"476_CR12","doi-asserted-by":"crossref","unstructured":"J. Hou, G. Wang, X. Chen, J. Xue, R. Zhu, H. Yang, Spatial-temporal attention res-TCN for skeleton-based dynamic hand gesture recognition. Proc. Fourth Int. Work. Observing Underst. Hands Action, 273\u2013286 (2018).","DOI":"10.1007\/978-3-030-11024-6_18"},{"key":"476_CR13","doi-asserted-by":"crossref","unstructured":"C. Lea, M. D. Flynn, R. Vidal, A. Reiter, G. D. Hager, Temporal convolutional networks for action segmentation and detection. Comput. Vis. Pattern Recognit. (CVPR), 1003\u20131012 (2017).","DOI":"10.1109\/CVPR.2017.113"},{"key":"476_CR14","doi-asserted-by":"crossref","unstructured":"D. Avola, M. Bernardi, L. Cinque, G. L. Foresti, C. Massaroni, Exploiting recurrent neural networks and leap motion controller for sign language and semaphoric gesture recognition. Computer Vision Pattern Recognition, 234\u2013245 (2018).","DOI":"10.1109\/TMM.2018.2856094"},{"key":"476_CR15","doi-asserted-by":"crossref","unstructured":"S. Yan, Y. Xiong, D. Lin, Spatial temporal graph convolutional networks for skeleton-based action recognition. Association for the Advance of Artificial Intelligence (AAAI), 7444\u20137452 (2018).","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"476_CR16","doi-asserted-by":"crossref","unstructured":"Q. D. Smedt, H. Wannous, J. P. Vandeborre, Skeleton-based dynamic hand gesture recognition. Comput. Vis. Pattern Recog. Workshops, 1206\u20131214 (2016).","DOI":"10.1109\/CVPRW.2016.153"},{"key":"476_CR17","unstructured":"Q. D. Smedt, H. Wannous, J. P. Vandeborre, J. Guerry, B. L. Saux, D. Filliat, Shrec\u201917 track: 3d hand gesture recognition using a depth and skeletal dataset. Eurographics, 86\u201397 (2018)."},{"key":"476_CR18","doi-asserted-by":"crossref","unstructured":"J. Weng, M. Liu, X. Jiang, J. Yuan, Deformable pose traversal convolution for 3d action and gesture recognition. Eur. Conf. Comput. Vis. (ECCV), 142\u2013157 (2018).","DOI":"10.1007\/978-3-030-01234-2_9"},{"key":"476_CR19","unstructured":"S. Y. Boulahia, E. Anquetil, F. Multon, R. Kulpa, in International conference on image processing. Dynamic hand gesture recognition based on 3D pattern assembled trajectories, (2017), pp. 1\u20136."},{"key":"476_CR20","unstructured":"G. Devineau, F. Moutarde, W. Xi, J. Yang, in IEEE International Conference on Automatic Face Gesture Recognition. Deep Learning for Hand Gesture Recognition on Skeletal Data, (2018), pp. 106\u2013113."},{"key":"476_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TMM.2018.2838320","volume":"20","author":"C. Yan","year":"2018","unstructured":"C. Yan, H. Xie, J. Chen, Z. Zha, X. Hao, Y. Zhang, Q. Dai, A fast uyghur text detector for complex background images. IEEE Trans. Multimed. 20:, 1\u20131 (2018).","journal-title":"IEEE Trans. Multimed"},{"key":"476_CR22","unstructured":"C. Yan, L. Li, C. Zhang, B. Liu, Q. Dai, Cross-modality bridging and knowledge transferring for image understanding. IEEE Trans. Multimed, 1\u20131 (2019)."},{"key":"476_CR23","doi-asserted-by":"crossref","unstructured":"C. Yan, Y. Tu, X. Wang, Y. Zhang, X. Hao, Q. Dai, Stat: Spatial-temporal attention mechanism for video captioning. IEEE Trans. Multimed, 1\u20131 (2019).","DOI":"10.1109\/TMM.2019.2924576"}],"container-title":["EURASIP Journal on Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13640-019-0476-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13640-019-0476-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13640-019-0476-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T15:50:15Z","timestamp":1664380215000},"score":1,"resource":{"primary":{"URL":"https:\/\/jivp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13640-019-0476-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,13]]},"references-count":23,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["476"],"URL":"https:\/\/doi.org\/10.1186\/s13640-019-0476-x","relation":{},"ISSN":["1687-5281"],"issn-type":[{"value":"1687-5281","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,13]]},"assertion":[{"value":"24 January 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"78"}}