Robust clothing-independent gait recognition using hybrid part-based gait features.

ABSTRACT: Recently, gait has been gathering extensive interest for the non-fungible position in applications. Although various methods have been proposed for gait recognition, most of them can only attain an excellent recognition performance when the probe and gallery gaits are in a similar condition. Once external factors (e.g., clothing variations) influence people's gaits and changes happen in human appearances, a significant performance degradation occurs. Hence, in our article, a robust hybrid part-based spatio-temporal feature learning method is proposed for gait recognition to handle this cloth-changing problem. First, human bodies are segmented into the affected and non/less unaffected parts based on the anatomical studies. Then, a well-designed network is proposed in our method to fo

SUBMITTER: Gao Z 

PROVIDER: S-EPMC9202625 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature