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On reducing the effect of covariate factors in gait recognition: a classifier ensemble method

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journal contribution
posted on 2015-07-01, 00:00 authored by Yu Guan, Chang-Tsun LiChang-Tsun Li, Fabio Roli
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.

History

Journal

IEEE transactions on pattern analysis and machine intelligence

Volume

37

Issue

7

Pagination

1521 - 1528

Publisher

Institute of Electrical and Electronics Engineers

Location

Piscataway, N.J.

eISSN

1939-3539

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2015, IEEE