Gait Recognition Under Different Clothing Conditions Via Deterministic Learning  

作　　者：Muqing Deng Cong Wang 

机构地区：[1]School of Automation,Guangdong University of Technology,Guangzhou 510006,China [2]School of Control Science and Engineering,Shandong University,Jinan 250100,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第6期1530-1532,共3页自动化学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(T2341019,61803133);the Guangzhou Basic and Applied Basic Research Project(202201010346,2023A04J0347);the Guangdong Basic and Applied Basic Research Foundation(2021A1515012635,2023A1515011245).

摘　　要：Dear Editor,This letter deals with the robustness problem of gait recognition method against maximum number of clothing conditions.By selecting four kinds of time-varying silhouette features,gait dynamics underlying different individuals’gait features is effectively modeled by radial basis function(RBF)neural networks through deterministic learning.This kind of dynamics information has little sensitivity to the variance between gait patterns under different clothing conditions.In order to eliminate the effect of clothing differences,the training patterns under different clothing conditions further constitute a uniform training dataset,containing all kinds of gait dynamics under different clothing conditions.A rapid recognition scheme is presented on published gait databases.Extensive experiments demonstrate the efficacy of the proposed method.

关 键 词：NETWORKS NEURAL INDIVIDUAL 

分 类 号：TS941[轻工技术与工程—服装设计与工程] TP183[自动化与计算机技术—控制理论与控制工程]