Gait recognition based on active energy image and parameter-adaptive Kernel PCA
ITAIC), 2011 6th IEEE Joint International（2011）
摘要
In this paper, we used gait silhouettes that provided by CASIA, and all we study are based on this database. Firstly, we normalized and centralized gait silhouettes and get the gait sequence, secondly, we extract the active regions by calculating the difference of two adjacent silhouettes images, and construct an AEI by accumulating these active regions, finally, using Kernel Principal Component Analysis (KPCA) method to analysis the AEI, and parameter optimization method used to determine the nuclear function of KPCA, and using SVM to classified and recognized gait. Experimental results show that such methods to be identified effective.
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关键词
gait sequence,active energy image,image processing,aei,casia,eigenvector,svm,pca,parameter adaptive kernel pca,kpca,centralized gait silhouettes,adjacent silhouettes images,gait analysis,object recognition,kpca nuclear function,gait recognition,parameter optimization method,principal component analysis,support vector machines,kernel principal component analysis,mathematical model,activation energy,feature extraction,kernel pca,eigenvectors,support vector machine
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