Making gait recognition robust to speed changes using mutual subspace method
Yumi Iwashita, Mafune Kakeshita, Hitoshi Sakano, Ryo Kurazume
Abstract
Mutual subspace method (MSM), which is one of image-based approaches, showed strong discrimination capability in gait recognition. In general, 2D image matrices are transformed into 1D image vectors to be used as input into MSM, and then principal component analysis (PCA) is applied to 1D vectors to generate a subspace. However, due to the high dimensionalities of 1D vectors, the evaluation accuracy of the covariance matrix in PCA is not high enough. This results in a decrease in performance, especially in case that speed difference between gallery and probe dataset is big. Thus in this paper we propose a method, which expands the MSM-based method, to recognize people with higher accuracy. The proposed method divides the human body area into multiple areas, followed by adaptive choice of areas that have high discrimination capability. Moreover, the proposed method utilizes the frieze pattern, which is one of gait features, as an additional input into MSM. The use of divided areas and the frieze pattern allows us to evaluate the covariance matrix with higher accuracy. In experiments we applied the proposed method to challenging databases with speed variations, and we show the effectiveness of the proposed method.
BibTeX
@inproceedings{icra2017_makinggaitrecogn,
title = {Making gait recognition robust to speed changes using mutual subspace method},
author = {Yumi Iwashita and Mafune Kakeshita and Hitoshi Sakano and Ryo Kurazume},
booktitle = {ICRA 2017},
year = {2017}
}