河南科技大学学报（自然科学版）2025，Vol.46Issue(2)：42-47,94,7.DOI:10.15926/j.cnki.issn1672-6871.2025.02.005
基于多级分层融合网络的步态识别方法
Method for Gait Recognition Based on Multi-level Hierarchical Fusion Network
摘要
Abstract
Gait recognition is a biometric technology that identifies individuals through their unique walking patterns.It is suitable for unconstrained environments and has broad application prospects.Although current methods focus on using body part based representations,they often overlook the hierarchical dependencies among local motion patterns.In this paper,we propose a multi-level hierarchical fusion model for extracting gait features from coarse to fine.Our framework integrates two strategies:multi-level hierarchical feature extraction and non-uniform hierarchical feature extraction.This enables fine-grained extraction of local features while emphasizing the interrelationships among local features.Verified by numerous experiments on widely recognized datasets,the method we proposed has been proven to be effective.While improving the model accuracy,this method also successfully maintains a reasonable balance in model complexity.关键词
局部特征/多级分层/不均匀/特征融合/步态识别Key words
local features/multi-level hierarchy/non-uniform/feature fusion/gait recognition分类
信息技术与安全科学引用本文复制引用
李哲,刘勇,刘中华,欧卫华..基于多级分层融合网络的步态识别方法[J].河南科技大学学报（自然科学版）,2025,46(2):42-47,94,7.基金项目
国家自然科学基金项目(U1504610,61962010,62262005) （U1504610,61962010,62262005）
