Guan, Yu, Li, Chang-Tsun and Roli, Fabio (2015) On reducing the effect of covariate factors in gait recognition : a classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 37 (Number 99). pp. 1521-1528. doi:10.1109/TPAMI.2014.2366766

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Abstract

Robust human gait recognition is challenging because of the presence of covariate factors such as carrying condition, clothing, walking surface, etc. In this paper, we model the effect of covariates as an unknown partial feature corruption problem. Since the locations of corruptions may differ for different query gaits, relevant features may become irrelevant when walking condition changes. In this case, it is difficult to train one fixed classifier that is robust to a large number of different covariates. To tackle this problem, we propose a classifier ensemble method based on the Random Subspace Method (RSM) and Majority Voting (MV). Its theoretical basis suggests it is insensitive to locations of corrupted features, and thus can generalize well to a large number of covariates. We also extend this method by proposing two strategies, i.e., Local Enhancing (LE) and Hybrid Decision-level Fusion (HDF) to suppress the ratio of false votes to true votes (before MV). The performance of our approach is competitive against the most challenging covariates like clothing, walking surface, and elapsed time. We evaluate our method on the USF dataset and OU-ISIR-B dataset, and it has much higher performance than other state-of-the-art algorithms.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Biometric identification, Gait in humans -- Measurement, Template matching (Digital image processing)
Journal or Publication Title:	IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher:	IEEE
ISSN:	0162-8828
Official Date:	1 July 2015
Dates:	Date Event 1 July 2015 Published 4 November 2014 Available 27 October 2014 Accepted 29 April 2014 Submitted
Date of first compliant deposit:	26 April 2016
Volume:	Volume 37
Number:	Number 99
Page Range:	pp. 1521-1528
DOI:	10.1109/TPAMI.2014.2366766
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access
Funder:	Royal Society (Great Britain)
Grant number:	IE120092

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