| Issue |
SHS Web Conf.
Volume 102, 2021
The 3rd ETLTC International Conference on Information and Communications Technology (ETLTC2021)
|
|
|---|---|---|
| Article Number | 04009 | |
| Number of page(s) | 4 | |
| Section | Applications in Computer Science | |
| DOI | https://doi.org/10.1051/shsconf/202110204009 | |
| Published online | 03 May 2021 | |
Real-time Hand-Gesture Recognition based on Deep Neural Network
Adaptive Systems Laboratory, University of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Japan
* Corresponding Author: Naoto Ageishi e-mail: s1250022@u-aizu.ac.jp
Hand gestures are a kind of nonverbal communication in which visible bodily actions are used to communicate important messages. Recently, hand gesture recognition has received significant attention from the research community for various applications, including advanced driver assistance systems, prosthetic, and robotic control. Therefore, accurate and fast classification of hand gesture is required. In this research, we created a deep neural network as the first step to develop a real-time camera-only hand gesture recognition system without electroencephalogram (EEG) signals. We present the system software architecture in a fair amount of details. The proposed system was able to recognize hand signs with an accuracy of 97.31%.
© The Authors, published by EDP Sciences, 2021
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