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On reducing the effect of covariate factors in gait recognition : a classifier ensemble method
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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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Official URL: http://dx.doi.org/10.1109/TPAMI.2014.2366766
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 | ||||||||||
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| 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: |
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| 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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