Abstract
This study investigates the potential of gait features as human gait recognition. Firstly, skeleton joints of twenty subjects obtained from Kinect are extracted as features and further selected using the optimized locally linear embedded approach. Next, multi-layer perceptron and support vector machine are employed as classifiers. Result showed that the combination of the optimized locally linear embedded with K=100 and d=94 and support vector machine regularization parameter C=0.001 and linear kernel attained highest accuracy rate in frontal view specifically 96.50% using 94 gait features.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
