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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 selection is an important pre-processing step for pattern recognition. It can discard irrelevant and redundant information that may not only affect a classifier’s performance, but also tell against system’s efficiency. Meanwhile, feature selection can help to identify the factors that most influence the recognition accuracy. The result can provide valuable clues to understand and reason what is the underlying distinctness among human gait-patterns. In this paper, we introduce a computationally-efficient solution to the problem of human gait feature selection. We show that feature selection based on mutual information can provide a realistic solution for high-dimensional human gait data. To assess the performance of the proposed approach, experiments are carried out based on a 73-dimensional model-based gait features set and a 64 by 64 pixels model-free gait symmetry map. The experimental results confirmed the effectiveness of the method, removing about 50% of the model-based features and 95% of the symmetry map’s pixels without significant accuracy loss, which outperforms correlation and ANOVA based methods.
Gait, Feature Selection, Entropy
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 (2007) Gait Feature Subset Selection by Mutual Information. IEEE Conference on Biometrics: Theory, Applications and Systems.

Record type: Conference or Workshop Item (Other)

Abstract

Feature selection is an important pre-processing step for pattern recognition. It can discard irrelevant and redundant information that may not only affect a classifier’s performance, but also tell against system’s efficiency. Meanwhile, feature selection can help to identify the factors that most influence the recognition accuracy. The result can provide valuable clues to understand and reason what is the underlying distinctness among human gait-patterns. In this paper, we introduce a computationally-efficient solution to the problem of human gait feature selection. We show that feature selection based on mutual information can provide a realistic solution for high-dimensional human gait data. To assess the performance of the proposed approach, experiments are carried out based on a 73-dimensional model-based gait features set and a 64 by 64 pixels model-free gait symmetry map. The experimental results confirmed the effectiveness of the method, removing about 50% of the model-based features and 95% of the symmetry map’s pixels without significant accuracy loss, which outperforms correlation and ANOVA based methods.

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More information

Published date: September 2007
Additional Information: Event Dates: Sep 2007
Venue - Dates: IEEE Conference on Biometrics: Theory, Applications and Systems, 2007-09-01
Keywords: Gait, Feature Selection, Entropy
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 264876
URI: http://eprints.soton.ac.uk/id/eprint/264876
PURE UUID: ab1a6ff2-49dd-4298-9329-87233bea37c8
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 19 Nov 2007 16:55
Last modified: 15 Oct 2019 00:57

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