PRWGEI: Poisson random walk based gait recognition

Recently, gait recognition has received much increased attention from biometrics researchers. Most of the literature shows that existing appearance based gait feature representation methods, however, suffer from clothing and carrying object covariate factors. Some new gait feature representations are proposed to overcome the issue of clothing and carrying covariate factors, e.g. Gait Entropy Image (GEnI). Even though these methods provide a good recognition rate for clothing and carrying covariate gait sequences, there is still a possibility of obtain the better recognition rate by using better appearance based gait feature representations. To the best of our knowledge, a Poison Random Walk (PRW) approach has not been considered to overcome the issue of clothing and carrying covariate factors' effects in gait feature representations. In this paper, we propose a novel method, PRW based Gait Energy Image (PRWGEI), to reduce the effect of covariate factors in gait feature representation. These PRWGEI features are projected into a low dimensional space by a Linear Discriminant Analysis (LDA) method to improve the discriminative power of the extracted features. The experimental results on the CASIA gait database (dataset B) show that our proposed method achieved a better recognition rate than other methods in the literature for clothing and carrying covariate factors.