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Access. Comput."],"published-print":{"date-parts":[[2016,5,7]]},"abstract":"<jats:p>\n            In this article, to utilize long-term dynamics over an isolated sign sequence, we propose a covariance matrix--based representation to naturally fuse information from multimodal sources. To tackle the drawback induced by the commonly used Riemannian metric, the proximity of covariance matrices is measured on the Grassmann manifold. However, the inherent Grassmann metric cannot be directly applied to the covariance matrix. We solve this problem by evaluating and selecting the most significant singular vectors of covariance matrices of sign sequences. The resulting compact representation is called the\n            <jats:italic>Grassmann covariance matrix<\/jats:italic>\n            . Finally, the Grassmann metric is used to be a kernel for the support vector machine, which enables learning of the signs in a discriminative manner. To validate the proposed method, we collect three challenging sign language datasets, on which comprehensive evaluations show that the proposed method outperforms the state-of-the-art methods both in accuracy and computational cost.\n          <\/jats:p>","DOI":"10.1145\/2897735","type":"journal-article","created":{"date-parts":[[2016,5,9]],"date-time":"2016-05-09T12:12:08Z","timestamp":1462795928000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":72,"title":["Isolated Sign Language Recognition with Grassmann Covariance Matrices"],"prefix":"10.1145","volume":"8","author":[{"given":"Hanjie","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Xiujuan","family":"Chai","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Xiaopeng","family":"Hong","sequence":"additional","affiliation":[{"name":"University of Oulu, Finland"}]},{"given":"Guoying","family":"Zhao","sequence":"additional","affiliation":[{"name":"University of Oulu, Finland"}]},{"given":"Xilin","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2016,5,7]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1137\/050637996"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2522848.2532592"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 8th International Joint Conference on Computer Vision, Imaging, and Computer Graphics Theory and Applications (VISAPP\u201913)","author":"Celebi Sait","year":"2013"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"e_1_2_1_5_1","unstructured":"China-Deaf-Assoc. 2003. 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