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：0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 08[工学] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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 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.