Gait Recognition by Cross Wavelet Transform and Graph Model

查看全文 作　　者：Sagar Arun [1]More;Pramod Jagan [1]Deore

机构地区：[1]Department of Electronics and Telecommunication Engineering,R.C.Patel Institute of Technology

出　　处：《IEEE/CAA Journal of Automatica Sinica》索引2018年第5卷第3期,共9页

摘　　要：In this paper, a multi-view gait based human recognition system using the fusion of two kinds of features is proposed.We use cross wavelet transform to extract dynamic feature and bipartite graph model to extract static feature which are coefficients of quadrature mirror filter(QMF)-graph wavelet filter bank. Feature fusion is done after normalization. For normalization of features, min-max rule is used and mean-variance method is used to find weights for normalized features. Euclidean distance between each feature vector and center of the cluster which is obtained by k-means clustering is used as similarity measure in Bayesian framework. Experiments performed on widely used CASIA B gait database show that, the fusion of these two feature sets preserve discriminant information. We report 99.90 % average recognition rate. 关键词：Binary sequences feature extraction identification of persons linear discriminant analysis(LDA)

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Gait Recognition by Cross Wavelet Transform and Graph Model

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