Dual phase learning for large scale video gait recognition

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.

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Official URL: http://dx.doi.org/10.1007/978-3-642-11301-7_50

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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