Vision-Based Hand Gesture Recognition Using Deep Learning Approach

Hand Gesture Recognition (HGR) serves as a fundamental way of communication and interaction for human being. HGR has the potential to transform Human Computer Interaction (HCI) and also entails many useful applications. For instance, HGR can be used to recognize sign language, which is a visual lang...

Full description

Saved in:

Bibliographic Details
Main Author:	Tan, Yong Soon
Format:	Thesis
Published:	2019
Subjects:	QA75.5-76.95 Electronic computers Computer science
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-mmu-ep.7745
record_format	uketd_dc
spelling	my-mmu-ep.77452020-09-21T18:46:05Z Vision-Based Hand Gesture Recognition Using Deep Learning Approach 2019-10 Tan, Yong Soon QA75.5-76.95 Electronic computers. Computer science Hand Gesture Recognition (HGR) serves as a fundamental way of communication and interaction for human being. HGR has the potential to transform Human Computer Interaction (HCI) and also entails many useful applications. For instance, HGR can be used to recognize sign language, which is a visual language and is used all over the world as a primary means of communication by the deaf and mute. Conventional HGR equipment like coloured glove to obtain accurate information about the gesture. It is intrusive, inconvenient and costly, which limits its applicability. Therefore, there is great need for vision-based HGR to simplify the communication and interaction process. However, vision-based HGR faces many challenges such as the variation in background, illumination, hand size, color of skin and also similarities among gestures. Vision-based HGR using traditional machine learning approaches typically involve multiple stages of complicated processing, such as hand-crafted feature extraction methods, which are usually designed to deal with certain challenges speciﬁcally. Hence, the effectiveness of the system and its ability to deal with different challenges across multiple datasets are heavily relied on the methods used. In contrast, deep learning approach such as CNN, adapts to different challenges via supervised learning. However, CNN network architecture is not fully explored and exploited for vision-based HGR. This study investigates the problem of vision-based HGR, with primary focus on deep learning approach. In this thesis, three deep neural network architectures are proposed for vision-based HGR, which are based on variants of Convolutional Neural Network, namely: (1) Wide ResNet, (2) Convolutional Neural Network with Spatial Pyramid Pooling (CNNSPP), and (3) MDenseNet. The proposed methods are evaluated on three public benchmark datasets. 2019-10 Thesis http://shdl.mmu.edu.my/7745/ http://library.mmu.edu.my/library2/diglib/mmuetd/ masters Multimedia University Faculty of Information Science & Technology
institution	Multimedia University
collection	MMU Institutional Repository
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Tan, Yong Soon Vision-Based Hand Gesture Recognition Using Deep Learning Approach
description	Hand Gesture Recognition (HGR) serves as a fundamental way of communication and interaction for human being. HGR has the potential to transform Human Computer Interaction (HCI) and also entails many useful applications. For instance, HGR can be used to recognize sign language, which is a visual language and is used all over the world as a primary means of communication by the deaf and mute. Conventional HGR equipment like coloured glove to obtain accurate information about the gesture. It is intrusive, inconvenient and costly, which limits its applicability. Therefore, there is great need for vision-based HGR to simplify the communication and interaction process. However, vision-based HGR faces many challenges such as the variation in background, illumination, hand size, color of skin and also similarities among gestures. Vision-based HGR using traditional machine learning approaches typically involve multiple stages of complicated processing, such as hand-crafted feature extraction methods, which are usually designed to deal with certain challenges speciﬁcally. Hence, the effectiveness of the system and its ability to deal with different challenges across multiple datasets are heavily relied on the methods used. In contrast, deep learning approach such as CNN, adapts to different challenges via supervised learning. However, CNN network architecture is not fully explored and exploited for vision-based HGR. This study investigates the problem of vision-based HGR, with primary focus on deep learning approach. In this thesis, three deep neural network architectures are proposed for vision-based HGR, which are based on variants of Convolutional Neural Network, namely: (1) Wide ResNet, (2) Convolutional Neural Network with Spatial Pyramid Pooling (CNNSPP), and (3) MDenseNet. The proposed methods are evaluated on three public benchmark datasets.
format	Thesis
qualification_level	Master's degree
author	Tan, Yong Soon
author_facet	Tan, Yong Soon
author_sort	Tan, Yong Soon
title	Vision-Based Hand Gesture Recognition Using Deep Learning Approach
title_short	Vision-Based Hand Gesture Recognition Using Deep Learning Approach
title_full	Vision-Based Hand Gesture Recognition Using Deep Learning Approach
title_fullStr	Vision-Based Hand Gesture Recognition Using Deep Learning Approach
title_full_unstemmed	Vision-Based Hand Gesture Recognition Using Deep Learning Approach
title_sort	vision-based hand gesture recognition using deep learning approach
granting_institution	Multimedia University
granting_department	Faculty of Information Science & Technology
publishDate	2019
_version_	1747829672334327808

Vision-Based Hand Gesture Recognition Using Deep Learning Approach

Similar Items