Individual Identification Using Gait Sequences under Different Covariate Factors

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Abstract

Recently, gait recognition for individual identification has received increased attention from biometrics researchers as gait can be captured at a distance using low-resolution capturing device. Human gait properties can be affected by different clothing and carrying objects (i.e. covariate factors). Most of the literature shows that these covariate factors give difficulties for individual identification based on gait. In this paper, we propose a novel method that generates dynamic and static feature templates of the sequences of silhouette images (Dynamic Static Silhouette Templates (DSSTs)) to overcome this issue. Here the DSST is calculated from Motion History Images (MHIs). The experimental results show that our method overcomes issues arising from differing clothing and the carrying of objects.