Conference Proceeding Article
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.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
Advances in Multimedia Modeling: 16th International Multimedia Modeling Conference, MMM 2010, Chongqing, China, January 6-8, 2010: Proceedings
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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. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/503
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