Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering

Weighted Fusion of Multi-Featured of Gait Recognition Algorithm

Authors
Hui Wang, Taijun Li, Haoli Zhou, Zezhong Yang
Corresponding Author
Hui Wang
Available Online October 2016.
DOI
10.2991/mmme-16.2016.71How to use a DOI?
Keywords
skeleton model; joint angle feature; feature fusion; SVM
Abstract

Gait recognition has become one of the hottest directions in study of long-range identification. Joint angle feature is an important gait feature, but extracting joint angle feature using traditional skeleton model is too idealistic. Therefore, a method of extracting joint angle feature based on skeleton model to remove the ending points was put forward. Given the low recognition rate of single feature, the paper would the joint angle fea-ture, GEI and discrete Hu moment invariants weighted feature fusion. The experimental results show that the new joint angle feature extraction and feature weighted fusion algorithm improves gait recognition perfor-mance.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering
Series
Advances in Engineering Research
Publication Date
October 2016
ISBN
10.2991/mmme-16.2016.71
ISSN
2352-5401
DOI
10.2991/mmme-16.2016.71How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Hui Wang
AU  - Taijun Li
AU  - Haoli Zhou
AU  - Zezhong Yang
PY  - 2016/10
DA  - 2016/10
TI  - Weighted Fusion of Multi-Featured of Gait Recognition Algorithm
BT  - Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering
PB  - Atlantis Press
SP  - 314
EP  - 318
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmme-16.2016.71
DO  - 10.2991/mmme-16.2016.71
ID  - Wang2016/10
ER  -