Gait recognition based on dynamic region analysis

Gait Energy Image (GEI) has been proved to be an effective identity signature in gait recognition. But previous approaches only treat this 2D image representation as a holistic feature and neglect the intrinsic dynamic characteristics of gait patterns. In this paper, we use variation analysis to obt...

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Bibliographic Details
Published inSignal processing Vol. 88; no. 9; pp. 2350 - 2356
Main Authors Yang, Xiaochao, Zhou, Yue, Zhang, Tianhao, Shu, Guang, Yang, Jie
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.09.2008
Elsevier Science
Subjects
Applied sciences
Detection, estimation, filtering, equalization, prediction
Dynamic region analysis
Enhanced gait energy image
Exact sciences and technology
Gabor-based discriminative common vectors analysis
Gait recognition
Information, signal and communications theory
Miscellaneous
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
Telecommunications and information theory
Enhanced gait energy image
Dynamic region analysis
Gabor-based discriminative common vectors analysis
Gait recognition
Performance evaluation
Discriminant analysis
Gait recognition;Dynamic region analysis;Enhanced gait energy image;Gabor-based discriminative common;vectors analysis
Dynamic characteristic
Noise reduction
Database
Subspace method
Image representation
Signal processing
Identification
Signal representation
Gait analysis
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Summary:Gait Energy Image (GEI) has been proved to be an effective identity signature in gait recognition. But previous approaches only treat this 2D image representation as a holistic feature and neglect the intrinsic dynamic characteristics of gait patterns. In this paper, we use variation analysis to obtain the dynamic region in GEI which reflects the walking manner of an individual. Based on this analysis, a dynamics weight mask is constructed to enhance the dynamic region and suppress the noises on the unimportant regions. The obtained gait representation called enhanced GEI (EGEI) is then represented in low dimensional subspace by Gabor-based discriminative common vectors analysis. We test the proposed approach on the USF HumanID Gait Database. Experimental results prove its effectiveness in terms of recognition rate.
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2008.03.006