Author(s):
1. SALISU IBRAHIM YUSUF:
Nile University of Nigeria, Department of Computer Science, Nigeria
2. STEVE A. ADESHINA:
Nile University of Nigeria, Department of Computer Science, Nigeria
3. MUOSSA MAHAMAT BOUKAR:
Nile University of Nigeria, Department of Computer Science, Nigeria
Abstract:
The human gait as a source of biometric data has improved identification at father distance, however it only full body gait data has been explored with deep learning models, which is resource demanding not always available due obstacles as in environment of deployment. In this research, we explored the use of upper gait analysis for identification with deep learning model convolutional recurrent neural network- Long Short Term Memory CRNN-LSTM and evaluate the reliability of using half gait against full gait, primary dataset was collected from 26 subject 12 females and 14 males. The result returns better accuracy with upper half gait than full body gait, hence, lower computation demand.
Page(s):
4968-4977
DOI:
DOI not available
Published:
Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 13, Year: 2022
Keywords:
Gait
,
Human Identification
,
Computer Visions
,
Gait Analysis
,
CRNN
References:
References are not available for this document.
Citations
Citations are not available for this document.