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J. Human. Robot."],"published-print":{"date-parts":[[2018,4]]},"abstract":"<jats:p> Gesture recognition plays an important role in human\u2013computer interaction. However, most existing methods are complex and time-consuming, which limit the use of gesture recognition in real-time environments. In this paper, we propose a static gesture recognition system that combines depth information and skeleton data to classify gestures. Through feature fusion, hand digit gestures of 0\u20139 can be recognized accurately and efficiently. According to the experimental results, the proposed gesture recognition system is effective and robust, which is invariant to complex background, illumination changes, reversal, structural distortion, rotation, etc. We have tested the system both online and offline which proved that our system is satisfactory to real-time requirements, and therefore it can be applied to gesture recognition in real-world human\u2013computer interaction systems. <\/jats:p>","DOI":"10.1142\/s0219843617500220","type":"journal-article","created":{"date-parts":[[2017,10,22]],"date-time":"2017-10-22T23:52:57Z","timestamp":1508716377000},"page":"1750022","source":"Crossref","is-referenced-by-count":15,"title":["A Novel Hand Gesture Recognition Based on High-Level Features"],"prefix":"10.1142","volume":"15","author":[{"given":"Jing","family":"Li","sequence":"first","affiliation":[{"name":"School of Information Engineering, Nanchang University, Nanchang 330031, P. R. 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