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http://dx.doi.org/10.5909/JBE.2018.23.2.246

CNN-Based Hand Gesture Recognition for Wearable Applications  

Moon, Hyeon-Chul (Korea Aerospace University, School of Electronics and Information Engineering)
Yang, Anna (Korea Aerospace University, School of Electronics and Information Engineering)
Kim, Jae-Gon (Korea Aerospace University, School of Electronics and Information Engineering)
Publication Information
Journal of Broadcast Engineering / v.23, no.2, 2018 , pp. 246-252 More about this Journal
Abstract
Hand gestures are attracting attention as a NUI (Natural User Interface) of wearable devices such as smart glasses. Recently, to support efficient media consumption in IoT (Internet of Things) and wearable environments, the standardization of IoMT (Internet of Media Things) is in the progress in MPEG. In IoMT, it is assumed that hand gesture detection and recognition are performed on a separate device, and thus provides an interoperable interface between these modules. Meanwhile, deep learning based hand gesture recognition techniques have been recently actively studied to improve the recognition performance. In this paper, we propose a method of hand gesture recognition based on CNN (Convolutional Neural Network) for various applications such as media consumption in wearable devices which is one of the use cases of IoMT. The proposed method detects hand contour from stereo images acquisitioned by smart glasses using depth information and color information, constructs data sets to learn CNN, and then recognizes gestures from input hand contour images. Experimental results show that the proposed method achieves the average 95% hand gesture recognition rate.
Keywords
MPEG-IoMT; Hand Gesture; Hand Contour; CNN; Gesture Recognition;
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Times Cited By KSCI : 2  (Citation Analysis)
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