Development of methodology for Malaysian sign language recognition

Development of Methodology for Malaysian Sign Language Recognition is undertaken on Malaysian Sign Language (MSL) aimed to reduce the challenge faced by the deaf community, while communicating in a normal society by providing a mechanism to translate sign language into words. Therefore, in this thes...

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Main Author:	Yona Falinie Abdul Gaus
Format:	Thesis
Language:	English English
Published:	2013
Subjects:	HV697-4959 Protection assistance and relief
Online Access:	https://eprints.ums.edu.my/id/eprint/41796/1/24%20PAGES.pdf https://eprints.ums.edu.my/id/eprint/41796/2/FULLTEXT.pdf
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id	my-ums-ep.41796
record_format	uketd_dc
spelling	my-ums-ep.417962024-12-04T07:13:12Z Development of methodology for Malaysian sign language recognition 2013 Yona Falinie Abdul Gaus HV697-4959 Protection, assistance and relief Development of Methodology for Malaysian Sign Language Recognition is undertaken on Malaysian Sign Language (MSL) aimed to reduce the challenge faced by the deaf community, while communicating in a normal society by providing a mechanism to translate sign language into words. Therefore, in this thesis, the main focus is on vision-based hand gesture recognition through investigation of each step including frame selection, skin segmentation, trajectory tracking and recognition. There were 112 isolated MSL chosen; each of the sign is performed six times by different signers, making six video sequence databases of MSL. From the six databases, five databases are used for training and one database is used for testing. In frame selection, only selected frames from the video are required to represent the respected signs. Then, skin likelihood model is built first to filter out skin region, which consists of hands and face region from non-skin region. Centroids, distances and orientations of hand skin region are collected as feature vectors. When overlapping between hand-face or hand-hand occurred, Linear Kalman Filter is used to distinguish the separate features. Each of the feature vectors is translated into chain code for the recognition stage using Hidden Markov Model. There will be six maximum logarithmic probability values that are produced representing the respective signs. In the testing stage, these six values need to be summed up in order to check the correct classification value, which is based on the highest logarithmic probability values. The recognition rate using HMM for testing data achieved up to 83.1%. 2013 Thesis https://eprints.ums.edu.my/id/eprint/41796/ https://eprints.ums.edu.my/id/eprint/41796/1/24%20PAGES.pdf text en public https://eprints.ums.edu.my/id/eprint/41796/2/FULLTEXT.pdf text en validuser masters Universiti Malaysia Sabah Sekolah Kejuruteraan dan Teknologi Maklumat
institution	Universiti Malaysia Sabah
collection	UMS Institutional Repository
language	English English
topic	HV697-4959 Protection assistance and relief
spellingShingle	HV697-4959 Protection assistance and relief Yona Falinie Abdul Gaus Development of methodology for Malaysian sign language recognition
description	Development of Methodology for Malaysian Sign Language Recognition is undertaken on Malaysian Sign Language (MSL) aimed to reduce the challenge faced by the deaf community, while communicating in a normal society by providing a mechanism to translate sign language into words. Therefore, in this thesis, the main focus is on vision-based hand gesture recognition through investigation of each step including frame selection, skin segmentation, trajectory tracking and recognition. There were 112 isolated MSL chosen; each of the sign is performed six times by different signers, making six video sequence databases of MSL. From the six databases, five databases are used for training and one database is used for testing. In frame selection, only selected frames from the video are required to represent the respected signs. Then, skin likelihood model is built first to filter out skin region, which consists of hands and face region from non-skin region. Centroids, distances and orientations of hand skin region are collected as feature vectors. When overlapping between hand-face or hand-hand occurred, Linear Kalman Filter is used to distinguish the separate features. Each of the feature vectors is translated into chain code for the recognition stage using Hidden Markov Model. There will be six maximum logarithmic probability values that are produced representing the respective signs. In the testing stage, these six values need to be summed up in order to check the correct classification value, which is based on the highest logarithmic probability values. The recognition rate using HMM for testing data achieved up to 83.1%.
format	Thesis
qualification_level	Master's degree
author	Yona Falinie Abdul Gaus
author_facet	Yona Falinie Abdul Gaus
author_sort	Yona Falinie Abdul Gaus
title	Development of methodology for Malaysian sign language recognition
title_short	Development of methodology for Malaysian sign language recognition
title_full	Development of methodology for Malaysian sign language recognition
title_fullStr	Development of methodology for Malaysian sign language recognition
title_full_unstemmed	Development of methodology for Malaysian sign language recognition
title_sort	development of methodology for malaysian sign language recognition
granting_institution	Universiti Malaysia Sabah
granting_department	Sekolah Kejuruteraan dan Teknologi Maklumat
publishDate	2013
url	https://eprints.ums.edu.my/id/eprint/41796/1/24%20PAGES.pdf https://eprints.ums.edu.my/id/eprint/41796/2/FULLTEXT.pdf
_version_	1818611429857558528

Development of methodology for Malaysian sign language recognition

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