Hand Gesture Recognition Using Artificial Neural Networks

Hand gesture has been part of human communication, where, young children usually communicate by using gesture before they can talk. Adults may have to also gesture if they need to or they are indeed mute or deaf. Thus the idea of teaching a machine to also learn gestures is very appealing due to its...

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Main Author:	Mustafa, Mohd Amrallah
Format:	Thesis
Language:	English English
Published:	2007
Subjects:	Gesture - Malaysia Neural networks - neurobiology
Online Access:	http://psasir.upm.edu.my/id/eprint/614/1/600415_fk_2007_2_abstrak_je__dh_pdf_.pdf
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id	my-upm-ir.614
record_format	uketd_dc
spelling	my-upm-ir.6142013-05-27T06:49:42Z Hand Gesture Recognition Using Artificial Neural Networks 2007-01 Mustafa, Mohd Amrallah Hand gesture has been part of human communication, where, young children usually communicate by using gesture before they can talk. Adults may have to also gesture if they need to or they are indeed mute or deaf. Thus the idea of teaching a machine to also learn gestures is very appealing due to its unique mode of communications. A reliable hand gesture recognition system will make the remote control become obsolete. However, many of the new techniques proposed are complicated to be implemented in real time, especially as a human machine interface. This thesis focuses on recognizing hand gesture in static posture. Since static hand postures not only can express some concepts, but also can act as special transition states in temporal gestures recognition, thus estimating static hand postures is in fact a big topics in gesture recognition. A database consists of 200 gesture images have been built, where five volunteers had help in the making of the database. The images were captured in a controlled environment and the postures are free from occlusion where the background is uncluttered and the hand is assumed to have been localized. A system was then built to recognize the hand gesture. The captured image will be first preprocessed in order to binarize the palm region, where Sobel edge detection technique has been employed, with later followed by morphological operation. A new feature extraction technique has been developed, based on horizontal and vertical states transition count, and the ratio of hand area with respect to the whole area of image. These set of features have been proven to have high intra class dissimilarity attributes. In order to have a system that can be easily trained, artificial neural networks has been chosen in the classification stage. A multilayer perceptron with back-propagation algorithm has been developed, thus the system is actually in-built to be used as a human machine interface. The gesture recognition system has been built and tested in Matlab, where simulations have shown promising results. The performance of recognition rate in this research is 95% which shows a major improvement in comparison to the available methods. Gesture - Malaysia Neural networks - neurobiology 2007-01 Thesis http://psasir.upm.edu.my/id/eprint/614/ http://psasir.upm.edu.my/id/eprint/614/1/600415_fk_2007_2_abstrak_je__dh_pdf_.pdf application/pdf en public masters Universiti Putra Malaysia Gesture - Malaysia Neural networks - neurobiology Faculty of Engineering English
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English English
topic	Gesture - Malaysia Neural networks - neurobiology
spellingShingle	Gesture - Malaysia Neural networks - neurobiology Mustafa, Mohd Amrallah Hand Gesture Recognition Using Artificial Neural Networks
description	Hand gesture has been part of human communication, where, young children usually communicate by using gesture before they can talk. Adults may have to also gesture if they need to or they are indeed mute or deaf. Thus the idea of teaching a machine to also learn gestures is very appealing due to its unique mode of communications. A reliable hand gesture recognition system will make the remote control become obsolete. However, many of the new techniques proposed are complicated to be implemented in real time, especially as a human machine interface. This thesis focuses on recognizing hand gesture in static posture. Since static hand postures not only can express some concepts, but also can act as special transition states in temporal gestures recognition, thus estimating static hand postures is in fact a big topics in gesture recognition. A database consists of 200 gesture images have been built, where five volunteers had help in the making of the database. The images were captured in a controlled environment and the postures are free from occlusion where the background is uncluttered and the hand is assumed to have been localized. A system was then built to recognize the hand gesture. The captured image will be first preprocessed in order to binarize the palm region, where Sobel edge detection technique has been employed, with later followed by morphological operation. A new feature extraction technique has been developed, based on horizontal and vertical states transition count, and the ratio of hand area with respect to the whole area of image. These set of features have been proven to have high intra class dissimilarity attributes. In order to have a system that can be easily trained, artificial neural networks has been chosen in the classification stage. A multilayer perceptron with back-propagation algorithm has been developed, thus the system is actually in-built to be used as a human machine interface. The gesture recognition system has been built and tested in Matlab, where simulations have shown promising results. The performance of recognition rate in this research is 95% which shows a major improvement in comparison to the available methods.
format	Thesis
qualification_level	Master's degree
author	Mustafa, Mohd Amrallah
author_facet	Mustafa, Mohd Amrallah
author_sort	Mustafa, Mohd Amrallah
title	Hand Gesture Recognition Using Artificial Neural Networks
title_short	Hand Gesture Recognition Using Artificial Neural Networks
title_full	Hand Gesture Recognition Using Artificial Neural Networks
title_fullStr	Hand Gesture Recognition Using Artificial Neural Networks
title_full_unstemmed	Hand Gesture Recognition Using Artificial Neural Networks
title_sort	hand gesture recognition using artificial neural networks
granting_institution	Universiti Putra Malaysia
granting_department	Faculty of Engineering
publishDate	2007
url	http://psasir.upm.edu.my/id/eprint/614/1/600415_fk_2007_2_abstrak_je__dh_pdf_.pdf
_version_	1747810273875460096

Hand Gesture Recognition Using Artificial Neural Networks

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