Human gait recognition based on frontal view using kinect features and orthogonal least square selection

This paper discussed the recognition of human gait for both frontal and oblique view based on orthogonal least square (OLS) as feature selection and Multi-Layer perceptron (MLP) as classifier. Firstly, kinect sensor was used to acquire several frames of human gait in both oblique and frontal view. N...

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Published in:2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
Main Author: Sahak R.; Tahir N.M.; Yassin A.I.; Kamaruzaman F.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2018
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050139746&doi=10.1109%2fISSPIT.2017.8388674&partnerID=40&md5=19e81929d1ef607ee4e8a52eaaf4c8c9
id 2-s2.0-85050139746
spelling 2-s2.0-85050139746
Sahak R.; Tahir N.M.; Yassin A.I.; Kamaruzaman F.
Human gait recognition based on frontal view using kinect features and orthogonal least square selection
2018
2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017


10.1109/ISSPIT.2017.8388674
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050139746&doi=10.1109%2fISSPIT.2017.8388674&partnerID=40&md5=19e81929d1ef607ee4e8a52eaaf4c8c9
This paper discussed the recognition of human gait for both frontal and oblique view based on orthogonal least square (OLS) as feature selection and Multi-Layer perceptron (MLP) as classifier. Firstly, kinect sensor was used to acquire several frames of human gait in both oblique and frontal view. Next, feature extraction is conducted via skeleton joint point in the 3D space known as direct-gait (D-G) feature. Further, feature selection is performed in identifying the significant D-G features using OLS labeled as OLS-G features. Then, the effectiveness of the OLS-G features in recognition of human based on their gait is evaluated using neural network classifier. Result attained proven that OLS-G feature in frontal view is indeed apt for recognition of human based on their gait with recognition accuracy of 90.6%. © 2017 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper

author Sahak R.; Tahir N.M.; Yassin A.I.; Kamaruzaman F.
spellingShingle Sahak R.; Tahir N.M.; Yassin A.I.; Kamaruzaman F.
Human gait recognition based on frontal view using kinect features and orthogonal least square selection
author_facet Sahak R.; Tahir N.M.; Yassin A.I.; Kamaruzaman F.
author_sort Sahak R.; Tahir N.M.; Yassin A.I.; Kamaruzaman F.
title Human gait recognition based on frontal view using kinect features and orthogonal least square selection
title_short Human gait recognition based on frontal view using kinect features and orthogonal least square selection
title_full Human gait recognition based on frontal view using kinect features and orthogonal least square selection
title_fullStr Human gait recognition based on frontal view using kinect features and orthogonal least square selection
title_full_unstemmed Human gait recognition based on frontal view using kinect features and orthogonal least square selection
title_sort Human gait recognition based on frontal view using kinect features and orthogonal least square selection
publishDate 2018
container_title 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
container_volume
container_issue
doi_str_mv 10.1109/ISSPIT.2017.8388674
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050139746&doi=10.1109%2fISSPIT.2017.8388674&partnerID=40&md5=19e81929d1ef607ee4e8a52eaaf4c8c9
description This paper discussed the recognition of human gait for both frontal and oblique view based on orthogonal least square (OLS) as feature selection and Multi-Layer perceptron (MLP) as classifier. Firstly, kinect sensor was used to acquire several frames of human gait in both oblique and frontal view. Next, feature extraction is conducted via skeleton joint point in the 3D space known as direct-gait (D-G) feature. Further, feature selection is performed in identifying the significant D-G features using OLS labeled as OLS-G features. Then, the effectiveness of the OLS-G features in recognition of human based on their gait is evaluated using neural network classifier. Result attained proven that OLS-G feature in frontal view is indeed apt for recognition of human based on their gait with recognition accuracy of 90.6%. © 2017 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
format Conference paper
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record_format scopus
collection Scopus
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