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Gait feature subset selection by mutual information

Gait feature subset selection by mutual information
Gait feature subset selection by mutual information
Feature subset selection is an important preprocessing step for pattern recognition, to discard irrelevant and redundant information, as well as to identify the most important attributes. In this paper, we investigate a computationally efficient solution to select the most important features for gait recognition. The specific technique applied is based on mutual information (MI), which evaluates the statistical dependence between two random variables and has an established relation with the Bayes classification error. Extending our earlier research, we show that a sequential selection method based on MI can provide an effective solution for high-dimensional human gait data. To assess the performance of the approach, experiments are carried out based on a 73-dimensional model-based gait features set and on a 64 by 64 pixels model-free gait symmetry map on the Southampton HiD Gait database. The experimental results confirm the effectiveness of the method, removing about 50% of the model-based features and 95% of the symmetry map’s pixels without significant loss in recognition capability, which outperforms correlation and analysis-of-variance-based methods
1083-4427
36-46
Guo, Baofeng
e62b04c7-167b-45d9-a400-67a631861f24
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Guo, Baofeng
e62b04c7-167b-45d9-a400-67a631861f24
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Guo, Baofeng and Nixon, Mark (2009) Gait feature subset selection by mutual information. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 39 (1), 36-46. (doi:10.1109/TSMCA.2008.2007977).

Record type: Article

Abstract

Feature subset selection is an important preprocessing step for pattern recognition, to discard irrelevant and redundant information, as well as to identify the most important attributes. In this paper, we investigate a computationally efficient solution to select the most important features for gait recognition. The specific technique applied is based on mutual information (MI), which evaluates the statistical dependence between two random variables and has an established relation with the Bayes classification error. Extending our earlier research, we show that a sequential selection method based on MI can provide an effective solution for high-dimensional human gait data. To assess the performance of the approach, experiments are carried out based on a 73-dimensional model-based gait features set and on a 64 by 64 pixels model-free gait symmetry map on the Southampton HiD Gait database. The experimental results confirm the effectiveness of the method, removing about 50% of the model-based features and 95% of the symmetry map’s pixels without significant loss in recognition capability, which outperforms correlation and analysis-of-variance-based methods

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Published date: January 2009
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 267605
URI: https://eprints.soton.ac.uk/id/eprint/267605
ISSN: 1083-4427
PURE UUID: 2766b7f7-d782-4b59-b865-134f39004369
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 19 Jun 2009 16:34
Last modified: 20 Jul 2019 01:28

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