Static hand gesture recognition system using a composite neural network

M. Ch Su, W. F. Jean, H. T. Chang

研究成果: 會議貢獻類型會議論文同行評審

27 引文 斯高帕斯(Scopus)

摘要

A system for the recognition of static hand gestures is developed. Applications of hand gesture recognition range from teleoperated control, to hand diagnostic and rehabilitation or to speaking aids for the deaf. We use two EMI-Gloves connected to an IBM compatible PC via HyperRectangular Composite Neural networks (HRCNNs) to implement a gesture recognition system. Using the supervised decision-directed learning (SDDL) algorithm, the HRCNNs can quickly learn the complex mapping of measurements of ten fingers' flex angles to corresponding categories. In addition, the values of the synaptic weights of the trained HRCNNs were utilized to extract a set of crisp IF-THEN classification rules. In order to increase tolerance on variations of measurements corrupted by noise or some other factors we propose a special scheme to fuzzify these crisp rules. The system is evaluated for the classification of 51 static hand gestures from 4 ″speakers″. The recognition accuracy for the testing set were 93.9%.

原文???core.languages.en_GB???
頁面786-792
頁數7
出版狀態已出版 - 1996
事件Proceedings of the 1996 5th IEEE International Conference on Fuzzy Systems. Part 3 (of 3) - New Orleans, LA, USA
持續時間: 8 9月 199611 9月 1996

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???event.eventtypes.event.conference???Proceedings of the 1996 5th IEEE International Conference on Fuzzy Systems. Part 3 (of 3)
城市New Orleans, LA, USA
期間8/09/9611/09/96

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