Shen, J. and Pang, H. and Tao, D. and Li, Xuelong (2010) Dual phase learning for large scale video gait recognition. In: Boll, S. and Tian, Q. and Zhang, L. and Zhang, Z. and Chen, Y.-P.P (eds.) Advances in Multimedia Modeling. Lecture Notes in Computer Science 5916. Berlin, Germany: Springer Verlag, pp. 500-510. ISBN 9783642113017.
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.
Metadata
| Item Type: | Book Section |
|---|---|
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Sarah Hall |
| Date Deposited: | 20 Jun 2013 13:37 |
| Last Modified: | 09 Aug 2023 12:33 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/7541 |
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