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On Reducing the effect of Covariate Factors in Gait Recognition: A Classifier Ensemble Method

Lookup NU author(s): Dr Yu Guan

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


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 nethod (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.


Publication metadata

Author(s): Guan Y, Li C-T, Roli F

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2015

Volume: 37

Issue: 7

Pages: 1521-1528

Print publication date: 05/06/2015

Online publication date: 04/11/2014

Acceptance date: 27/10/2014

ISSN (print): 0162-8828

ISSN (electronic): 1939-3539

Publisher: IEEE

URL: https://doi.org/10.1109/TPAMI.2014.2366766

DOI: 10.1109/TPAMI.2014.2366766


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