Human gait recognition using skeleton joint coordinates with orthogonal least square and locally linear embedded techniques

• Published in: International Journal of Simulation: Systems, Science and Technology
• Main Author: Sahak R.; Tahir N.M.; Yassin A.I.M.; Kamaruzaman F.H.; Al Misreb A.
• Format: Article
• Language: English
• Published: UK Simulation Society 2018
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056424386&doi=10.5013%2fIJSSST.a.19.05.25&partnerID=40&md5=d1dd7df3f4fe198e64ddc371c1061c3d

This study investigated the skeleton points in the three dimensional (3D) space using Kinect sensor to be utilised as recognition of human based on their walking gait in frontal view. Firstly, walking gait of 30 subjects is captured using Kinect sensor. Next, all the twenty skeleton joints in the 3D space within one full gait cycle of each subjects are extracted as features. Further, the significant features amongst the skeleton joints are extracted using Orthogonal Least Square (OLS) technique followed by Locally Linear Embedded (LLE) as feature selection. The effectiveness of OLS as feature extraction along with LLE as feature selection is evaluated using Support Vector Machine as the machine classifier. Classification results attained for human gait recognition are 98.67% and 97% based on RBF and polynomial kernel respectively This proven that the significant gait features identified using OLS and LLE techniques are indeed suitable for gait recognition purpose. © 2018, UK Simulation Society. All rights reserved.