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

Yu Guan, Chang Tsun Li, Fabio Roli

    Research output: Contribution to journalArticlepeer-review

    87 Citations (Scopus)
    14 Downloads (Pure)

    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.

    Original languageEnglish
    Article number6945828
    Pages (from-to)1521-1528
    Number of pages8
    JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
    Volume37
    Issue number7
    DOIs
    Publication statusPublished - 01 Jul 2015

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