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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
https://eprints.ums.edu.my/id/eprint/41796/
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spelling my.ums.eprints.417962024-12-04T07:13:12Z https://eprints.ums.edu.my/id/eprint/41796/ Development of methodology for Malaysian sign language recognition 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 NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/41796/1/24%20PAGES.pdf text en https://eprints.ums.edu.my/id/eprint/41796/2/FULLTEXT.pdf Yona Falinie Abdul Gaus (2013) Development of methodology for Malaysian sign language recognition. Masters thesis, Universiti Malaysia Sabah.
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
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
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
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
https://eprints.ums.edu.my/id/eprint/41796/
_version_ 1817843844754964480
score 13.223943

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