Hand Gesture Recognition Using 3D-CNN Model

被引：24

作者：

Al-Hammadi, Muneer ^{[1
]}

Muhammad, Ghulam ^{[1
]}

Abdul, Wadood ^{[1
]}

Alsulaiman, Mansour ^{[1
]}

Hossain, M. Shamim ^{[2
]}

机构：

[1] King Saud Univ, Dept Comp Engn, CCIS, Riyadh, Saudi Arabia

[2] King Saud Univ, Dept Software Engn, CCIS, Riyadh, Saudi Arabia

来源：

IEEE CONSUMER ELECTRONICS MAGAZINE | 2020年 / 9卷 / 01期

关键词：

Gesture recognition; Training; Assistive technology; Three-dimensional displays; Kernel; Consumer electronics; Robustness;

D O I：

10.1109/MCE.2019.2941464

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Automatic hand gesture recognition is the most important part of sign language translation. Its importance increases with the growth of deaf and hard of hearing population and cognitive computing. In this article, we propose an efficient system for automatic hand gesture recognition based on deep learning. The proposed system is based on a convolutional neural network (CNN). It employs a transfer learning of 3D CNN for hand gesture recognition. Three different datasets are used to evaluate the proposed system in signer dependent and signer independent modes.

引用

页码：95 / 101

页数：7

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