DocumentCode :
2337749
Title :
Automatic Gait Recognition Using Kernel Principal Component Analysis
Author :
Chen, Xiang-Tao ; Fan, Zhi-Hui ; Wang, Hui ; Li, Zhe-Qing
Author_Institution :
Modern Educ. Technol. & Inf. Center, Henan Univ. of Sci. & Technol., Luoyang, China
fYear :
2010
fDate :
23-25 April 2010
Firstpage :
1
Lastpage :
4
Abstract :
Gait is one of the biometric technologies which can be identified as an individual by his/her walking style. This paper proposes an effective gait recognition method based on mean gait energy image(MGEI) which utilizes kernel principal component analysis(KPCA). KPCA is capable of capturing part of the high-order statistics which are particularly important for MGEI structure. MGEI is calculated from gait cycle. KPCA can make use of the high correlation between the training samples and test samples for feature extraction by selecting the proper kernel function. And Euclidean distance of covariance weighted reciprocal is designed as the classifier. Comprehensive experiments are carried out on CASIA gait database and USF HumanID database. The experimental results demonstrate that the proposed approach has an encouraging recognition performance.
Keywords :
authorisation; biometrics (access control); feature extraction; gait analysis; principal component analysis; CASIA gait database; Euclidean distance; KPCA; MGEI structure; USF HumanID database; automatic gait recognition; biometric technologies; covariance weighted reciprocal; feature extraction; high-order statistics; kernel principal component analysis; mean gait energy image; Biometrics; Feature extraction; Image analysis; Image recognition; Kernel; Legged locomotion; Principal component analysis; Spatial databases; Statistics; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Engineering and Computer Science (ICBECS), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5315-3
Type :
conf
DOI :
10.1109/ICBECS.2010.5462298
Filename :
5462298
Link To Document :
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