Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
1-2010
Abstract
Accurate gait recognition from video is a complex process involving heterogenous features, and is still being developed actively. This article introduces a novel framework, called GC2F, for effective and efficient gait recognition and classification. Adopting a ”refinement-and-classification” principle, the framework comprises two components: 1) a classifier to generate advanced probabilistic features from low level gait parameters; and 2) a hidden classifier layer (based on multilayer perceptron neural network) to model the statistical properties of different subject classes. To validate our framework, we have conducted comprehensive experiments with a large test collection, and observed significant improvements in identification accuracy relative to other state-of-the-art approaches.
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Publication
Advances in Multimedia Modeling: 16th International Multimedia Modeling Conference, MMM 2010, Chongqing, China, January 6-8, 2010: Proceedings
Volume
5916
First Page
500
Last Page
510
ISBN
9783642113017
Identifier
10.1007/978-3-642-11301-7_50
Publisher
Springer Verlag
City or Country
Berlin
Citation
SHEN, Jialie; PANG, Hwee Hwa; TAO, Dacheng; and LI, Xuelong.
Dual Phase Learning for Large Scale Video Gait Recognition. (2010). Advances in Multimedia Modeling: 16th International Multimedia Modeling Conference, MMM 2010, Chongqing, China, January 6-8, 2010: Proceedings. 5916, 500-510.
Available at: https://ink.library.smu.edu.sg/sis_research/503
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://dx.doi.org/10.1007/978-3-642-11301-7_50
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons