PRWGEI: Poisson random walk based gait recognition

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

Abstract

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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages662-667
Number of pages6
Publication statusPublished - 4 Sep 2011
Event2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA), - Dubrovnik
Duration: 4 Sep 2011 → …

Conference

Conference2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA),
Period4/09/11 → …

Fingerprint

Discriminant analysis
Biometrics
Entropy

Cite this

@inproceedings{306818bc587c4cc1a3e6ad7ae82c8467,
title = "PRWGEI: Poisson random walk based gait recognition",
abstract = "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.",
author = "P Yogarajah and Joan Condell and G Prasad",
year = "2011",
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day = "4",
language = "English",
isbn = "978-1-4577-0841-1",
pages = "662--667",
booktitle = "Unknown Host Publication",

}

Yogarajah, P, Condell, J & Prasad, G 2011, PRWGEI: Poisson random walk based gait recognition. in Unknown Host Publication. pp. 662-667, 2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA), 4/09/11.

PRWGEI: Poisson random walk based gait recognition. / Yogarajah, P; Condell, Joan; Prasad, G.

Unknown Host Publication. 2011. p. 662-667.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - PRWGEI: Poisson random walk based gait recognition

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AU - Condell, Joan

AU - Prasad, G

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N2 - 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.

AB - 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.

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