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Malaysian Sign Language Recognition Using Skeleton Data with Neural Network

This paper proposed the Backpropagation Neural Network (BNN) for recognition of Malaysian Sign Language. The image acquisition is done with the help of skeletal tracking using a Kinect camera. The data samples tested there are 15 dynamic signs taken from the Malaysian Sign Language (MySL). Pre-proce...

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Main Authors: Sutarman, na, Jasni, Mohamad Zain, Mazlina, Abdul Majid, Arief, Hermawan
Format: Conference or Workshop Item
Language:English
Published: 2015
Subjects:
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/10543/1/Malaysian%20Sign%20Language%20Recognition%20Using%20Skeleton%20Data%20with%20Neural%20Network.pdf
http://umpir.ump.edu.my/id/eprint/10543/
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spelling my.ump.umpir.105432018-05-22T02:06:35Z http://umpir.ump.edu.my/id/eprint/10543/ Malaysian Sign Language Recognition Using Skeleton Data with Neural Network Sutarman, na Jasni, Mohamad Zain Mazlina, Abdul Majid Arief, Hermawan QA76 Computer software This paper proposed the Backpropagation Neural Network (BNN) for recognition of Malaysian Sign Language. The image acquisition is done with the help of skeletal tracking using a Kinect camera. The data samples tested there are 15 dynamic signs taken from the Malaysian Sign Language (MySL). Pre-processing in this study was based on skeleton tracking the joints, to produce 3D coordinates X, Y, Z. The sample of Data 3D coordinates X, Y, and Z is taken a value relative to the Spine and head. The feature extraction is done by normalizing the position and size of the user, by taking eight out of 20 joints contributed in identifying the movement with the hand, left hand, right hand, left wrist, right wrist, left elbow, right elbow, Spine, Head. We use spherical coordinate conversion process and grouping frame used mean function. Two different approaches are evaluated: (1) the Cartesian coordinates (X, Y, and Z) of the six used joints and number of group's frames 14. The classifier used is the Neural Network (Cartesian + ANN); (2) the spherical coordinates (X, Y, and Z) and number of group's frame 14. The classifier used is Neural Network (Spherical+ANN). The experimental results of the Cartesian coordinate, and Artificial Neural Network (ANN) is the best of the neuron hidden layer 150, the best average recognition rate 80.54% with ± 2.07%, maximum 82.61% and minimum 78.47%. The increase at the recognition rate of Cartesian Coordinate+ANN with Spherical coordinates is 12.93%. 2015 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/10543/1/Malaysian%20Sign%20Language%20Recognition%20Using%20Skeleton%20Data%20with%20Neural%20Network.pdf Sutarman, na and Jasni, Mohamad Zain and Mazlina, Abdul Majid and Arief, Hermawan (2015) Malaysian Sign Language Recognition Using Skeleton Data with Neural Network. In: International Conference on Science in Information Technology (ICSITECH 2015), 27-28 Oct 2015 , Yogjakarta, Indonesia. . (Unpublished)
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Sutarman, na
Jasni, Mohamad Zain
Mazlina, Abdul Majid
Arief, Hermawan
Malaysian Sign Language Recognition Using Skeleton Data with Neural Network
description This paper proposed the Backpropagation Neural Network (BNN) for recognition of Malaysian Sign Language. The image acquisition is done with the help of skeletal tracking using a Kinect camera. The data samples tested there are 15 dynamic signs taken from the Malaysian Sign Language (MySL). Pre-processing in this study was based on skeleton tracking the joints, to produce 3D coordinates X, Y, Z. The sample of Data 3D coordinates X, Y, and Z is taken a value relative to the Spine and head. The feature extraction is done by normalizing the position and size of the user, by taking eight out of 20 joints contributed in identifying the movement with the hand, left hand, right hand, left wrist, right wrist, left elbow, right elbow, Spine, Head. We use spherical coordinate conversion process and grouping frame used mean function. Two different approaches are evaluated: (1) the Cartesian coordinates (X, Y, and Z) of the six used joints and number of group's frames 14. The classifier used is the Neural Network (Cartesian + ANN); (2) the spherical coordinates (X, Y, and Z) and number of group's frame 14. The classifier used is Neural Network (Spherical+ANN). The experimental results of the Cartesian coordinate, and Artificial Neural Network (ANN) is the best of the neuron hidden layer 150, the best average recognition rate 80.54% with ± 2.07%, maximum 82.61% and minimum 78.47%. The increase at the recognition rate of Cartesian Coordinate+ANN with Spherical coordinates is 12.93%.
format Conference or Workshop Item
author Sutarman, na
Jasni, Mohamad Zain
Mazlina, Abdul Majid
Arief, Hermawan
author_facet Sutarman, na
Jasni, Mohamad Zain
Mazlina, Abdul Majid
Arief, Hermawan
author_sort Sutarman, na
title Malaysian Sign Language Recognition Using Skeleton Data with Neural Network
title_short Malaysian Sign Language Recognition Using Skeleton Data with Neural Network
title_full Malaysian Sign Language Recognition Using Skeleton Data with Neural Network
title_fullStr Malaysian Sign Language Recognition Using Skeleton Data with Neural Network
title_full_unstemmed Malaysian Sign Language Recognition Using Skeleton Data with Neural Network
title_sort malaysian sign language recognition using skeleton data with neural network
publishDate 2015
url http://umpir.ump.edu.my/id/eprint/10543/1/Malaysian%20Sign%20Language%20Recognition%20Using%20Skeleton%20Data%20with%20Neural%20Network.pdf
http://umpir.ump.edu.my/id/eprint/10543/
_version_ 1643666420388593664
score 13.211869

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