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

IEEE Transactions on Pattern Analysis and Machine Intelligence
Yu GuanFabio Roli

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 algori...Continue Reading

References

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Citations

Apr 29, 2016·Journal of Forensic Sciences·Martin SandauErik B Simonsen
Feb 7, 2014·Journal of Biomedical Semantics·Aron HenrikssonMartin Duneld
Jul 15, 2017·IEEE Transactions on Pattern Analysis and Machine Intelligence·Xin ChenWei Lu
Nov 22, 2019·International Journal of Neural Systems·Xiuhui Wang, Wei Qi Yan
Mar 29, 2020·NPJ Digital Medicine·Ignacio Perez-PozueloLuis Fernandez-Luque
Feb 23, 2019·Journal of Forensic Sciences·Ioana MacoveciucHervé Borrion
Feb 18, 2021·NPJ Digital Medicine·Ignacio Perez-PozueloLuis Fernandez-Luque

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