Hand gesture recognition based on convolutional neural networks

Yu-lu Hu; Lian-ming Wang

doi:10.1117/12.2291737

15 November 2017 Hand gesture recognition based on convolutional neural networks

Yu-lu Hu, Lian-ming Wang

Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106051S (2017) https://doi.org/10.1117/12.2291737
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China

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.

Citation Download Citation

Yu-lu Hu and Lian-ming Wang "Hand gesture recognition based on convolutional neural networks", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106051S (15 November 2017); https://doi.org/10.1117/12.2291737

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