Citation

Tan, Yong Soon and Lim, Kian Ming and Lee, Chin Poo (2021) Wide Residual Network for Vision-based Static Hand Gesture Recognition. IAENG International Journal of Computer Science, 48 (4). pp. 906-914. ISSN 1819-9224

Text S2021_J328.pdf Restricted to Repository staff only Download (3MB)

Abstract

Hand gesture is a communication tool that allows messages to be conveyed, actions to be performed through hand gestures. Hence, it has the ability to simplify communication and enhance human computer interaction. This paper proposed Wide Residual Network for static hand gesture recognition. WRN improves feature propagation and gradient flows by utilizing shortcut connection in residual block. Wide residual block further improves upon residual block by increasing the width of the network and improving feature reuse, and thereby allowing the depth of the network to be trimmed and fewer trainable parameters to be learned. The network is experimented on three public datasets and compared with existing convolutional neural network (CNN) variants proposed for static hand gesture recognition. Experimental results show Wide Residual Network outperforms the existing CNN variants proposed for hand gesture recognition.

Item Type:	Article
Uncontrolled Keywords:	Hand gesture recognition, Sign language recognition, Convolutional Neural Network (CNN), Wide Residual Network
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	22 Feb 2022 03:13
Last Modified:	22 Feb 2022 03:13
URII:	http://shdl.mmu.edu.my/id/eprint/9973

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