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Proceedings Paper

Hand gesture recognition based on convolutional neural networks
Author(s): Yu-lu Hu; Lian-ming Wang
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Paper Abstract

Hand gesture has been considered a natural, intuitive and less intrusive way for Human-Computer Interaction (HCI). Although many algorithms for hand gesture recognition have been proposed in literature, robust algorithms have been pursued. A recognize algorithm based on the convolutional neural networks is proposed to recognize ten kinds of hand gestures, which include rotation and turnover samples acquired from different persons. When 6000 hand gesture images were used as training samples, and 1100 as testing samples, a 98% recognition rate was achieved with the convolutional neural networks, which is higher than that with some other frequently-used recognition algorithms.

Paper Details

Date Published: 15 November 2017
PDF: 6 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106051S (15 November 2017); doi: 10.1117/12.2291737
Show Author Affiliations
Yu-lu Hu, Northeast Normal Univ. (China)
Lian-ming Wang, Northeast Normal Univ. (China)

Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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