期刊
2017 IEEE SYMPOSIUM ON COMPUTER APPLICATIONS & INDUSTRIAL ELECTRONICS (ISCAIE)
卷 -, 期 -, 页码 157-162出版社
IEEE
关键词
Frontal gait; kinect; locally linear embedded; support vector machine; multi-layer perceptron
类别
资金
- Institute of Research Management and Innovation (IRMI), Universiti Teknologi MARA (UiTM) [600-RMI/DANA 5/3/PSI (195/2013)]
- Ministry of Higher Education (MOHE) Malaysia under the Niche Research Grant Scheme (NRGS) Project [600-RMI/NRGS 5/3]
- MOHE
- Faculty of Electrical Engineering UiTM Shah Alam
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据