Human gait recognition using orthogonal least square as feature selection
This study investigates the potential gait features that are related to human recognition using orthogonal least square (OLS). Firstly, video of 30 subjects walking in oblique view was recorded using Kinect. Next, all 20 skeleton joints in 3D space were extracted and further selected using OLS. Addi...
Published in: | Indonesian Journal of Electrical Engineering and Computer Science |
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Institute of Advanced Engineering and Science
2019
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2-s2.0-85074948400 Sahak R.; Tahir N.M.; Yassin A.I.M.; Kamaruzaman F.H. Human gait recognition using orthogonal least square as feature selection 2019 Indonesian Journal of Electrical Engineering and Computer Science 17 3 10.11591/ijeecs.v17.i3.pp1355-1361 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074948400&doi=10.11591%2fijeecs.v17.i3.pp1355-1361&partnerID=40&md5=60704e9fdbcf392c1f849276542cbcd8 This study investigates the potential gait features that are related to human recognition using orthogonal least square (OLS). Firstly, video of 30 subjects walking in oblique view was recorded using Kinect. Next, all 20 skeleton joints in 3D space were extracted and further selected using OLS. Additionally, SVM with linear, polynomial and radial basis function (RBF) kernel was used to classify the selected features. As consequences, OLS was proven to be able to identify the significant features using all three kernels of SVM since all recognition accuracy attained is higher as compared to the original gait features. Results attained showed that the highest recognition accuracy was 90.67% using 48 skeleton joint points for SVM with linear as kernel, followed by 46 skeleton joint points for SVM with RBF kernel namely 88.33% and accuracy of 86.33% for 38 skeleton joint points using polynomial kernel. Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved. Institute of Advanced Engineering and Science 25024752 English Article All Open Access; Gold Open Access |
author |
Sahak R.; Tahir N.M.; Yassin A.I.M.; Kamaruzaman F.H. |
spellingShingle |
Sahak R.; Tahir N.M.; Yassin A.I.M.; Kamaruzaman F.H. Human gait recognition using orthogonal least square as feature selection |
author_facet |
Sahak R.; Tahir N.M.; Yassin A.I.M.; Kamaruzaman F.H. |
author_sort |
Sahak R.; Tahir N.M.; Yassin A.I.M.; Kamaruzaman F.H. |
title |
Human gait recognition using orthogonal least square as feature selection |
title_short |
Human gait recognition using orthogonal least square as feature selection |
title_full |
Human gait recognition using orthogonal least square as feature selection |
title_fullStr |
Human gait recognition using orthogonal least square as feature selection |
title_full_unstemmed |
Human gait recognition using orthogonal least square as feature selection |
title_sort |
Human gait recognition using orthogonal least square as feature selection |
publishDate |
2019 |
container_title |
Indonesian Journal of Electrical Engineering and Computer Science |
container_volume |
17 |
container_issue |
3 |
doi_str_mv |
10.11591/ijeecs.v17.i3.pp1355-1361 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074948400&doi=10.11591%2fijeecs.v17.i3.pp1355-1361&partnerID=40&md5=60704e9fdbcf392c1f849276542cbcd8 |
description |
This study investigates the potential gait features that are related to human recognition using orthogonal least square (OLS). Firstly, video of 30 subjects walking in oblique view was recorded using Kinect. Next, all 20 skeleton joints in 3D space were extracted and further selected using OLS. Additionally, SVM with linear, polynomial and radial basis function (RBF) kernel was used to classify the selected features. As consequences, OLS was proven to be able to identify the significant features using all three kernels of SVM since all recognition accuracy attained is higher as compared to the original gait features. Results attained showed that the highest recognition accuracy was 90.67% using 48 skeleton joint points for SVM with linear as kernel, followed by 46 skeleton joint points for SVM with RBF kernel namely 88.33% and accuracy of 86.33% for 38 skeleton joint points using polynomial kernel. Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved. |
publisher |
Institute of Advanced Engineering and Science |
issn |
25024752 |
language |
English |
format |
Article |
accesstype |
All Open Access; Gold Open Access |
record_format |
scopus |
collection |
Scopus |
_version_ |
1809677905626660864 |