A gait recognition method using L1-PCA and LDA

Gait is one of biometric technologies which can be identified at a distance or at low resolution. This paper proposes a gait recognition method using PCA based on L1-norm maximization and LDA. The gait pattern is described by the periodic sequence width images, which contain the static and dynamic g...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 6; pp. 3198 - 3203
Main Authors Han Su, Zhi-Wu Liao, Guo-Yue Chen
Format Conference Proceeding
LanguageEnglish
Japanese
Published IEEE 01.07.2009
Subjects
Biometric
Biometrics
Computer vision
Cybernetics
Feature extraction
gait recognition
Humans
L1-PCA
LDA
Legged locomotion
Linear discriminant analysis
Machine learning
Principal component analysis
Scattering
width analysis
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Summary:Gait is one of biometric technologies which can be identified at a distance or at low resolution. This paper proposes a gait recognition method using PCA based on L1-norm maximization and LDA. The gait pattern is described by the periodic sequence width images, which contain the static and dynamic gait feature. L1-PCA is adopted to represent these features and LDA is used to analyze and classify the features. L1-PCA tries to find projections through maximizing L1-norm and LDA tries to find the projective direction which minimize the within-class scatter of examples and maximize between-class scatter. L1-PCA and LDA can keep gait feature and reduce the dimension of the feature. The performance of our approach was tested on the gait databases. The result of experiment proves that our method is effective for the recognition of gait sequence which is lower image resolution and noisy data.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212776