Gait Recognition by Cross Wavelet Transform and Graph Model

作     者：Sagar Arun More Pramod Jagan Deore 

作者机构：Department of Electronics and Telecommunication EngineeringR.C.Patel Institute of Technology 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2018年第5卷第3期

页      面：718-726页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 

主　　题：Binary sequences feature extraction identification of persons linear discriminant analysis(LDA) 

摘      要：In this paper, a multi-view gait based human recognition system using the fusion of two kinds of features is *** 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.